diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 6093c6c3fd0fc..0eefa85211194 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -82,6 +82,17 @@ def _convert_params(sql, params):
     return args
 
 
+def _process_parse_dates_argument(parse_dates):
+    """Process parse_dates argument for read_sql functions"""
+    # handle non-list entries for parse_dates gracefully
+    if parse_dates is True or parse_dates is None or parse_dates is False:
+        parse_dates = []
+
+    elif not hasattr(parse_dates, '__iter__'):
+        parse_dates = [parse_dates]
+    return parse_dates
+
+
 def _handle_date_column(col, utc=None, format=None):
     if isinstance(format, dict):
         return to_datetime(col, errors='ignore', **format)
@@ -96,8 +107,7 @@ def _handle_date_column(col, utc=None, format=None):
         elif is_datetime64tz_dtype(col):
             # coerce to UTC timezone
             # GH11216
-            return (to_datetime(col, errors='coerce')
-                    .astype('datetime64[ns, UTC]'))
+            return to_datetime(col, utc=True)
         else:
             return to_datetime(col, errors='coerce', format=format, utc=utc)
 
@@ -107,27 +117,18 @@ def _parse_date_columns(data_frame, parse_dates):
     Force non-datetime columns to be read as such.
     Supports both string formatted and integer timestamp columns.
     """
-    # handle non-list entries for parse_dates gracefully
-    if parse_dates is True or parse_dates is None or parse_dates is False:
-        parse_dates = []
-
-    if not hasattr(parse_dates, '__iter__'):
-        parse_dates = [parse_dates]
-
-    for col_name in parse_dates:
-        df_col = data_frame[col_name]
-        try:
-            fmt = parse_dates[col_name]
-        except TypeError:
-            fmt = None
-        data_frame[col_name] = _handle_date_column(df_col, format=fmt)
+    parse_dates = _process_parse_dates_argument(parse_dates)
 
-    # we want to coerce datetime64_tz dtypes for now
+    # we want to coerce datetime64_tz dtypes for now to UTC
     # we could in theory do a 'nice' conversion from a FixedOffset tz
     # GH11216
     for col_name, df_col in data_frame.iteritems():
-        if is_datetime64tz_dtype(df_col):
-            data_frame[col_name] = _handle_date_column(df_col)
+        if is_datetime64tz_dtype(df_col) or col_name in parse_dates:
+            try:
+                fmt = parse_dates[col_name]
+            except TypeError:
+                fmt = None
+            data_frame[col_name] = _handle_date_column(df_col, format=fmt)
 
     return data_frame
 
@@ -139,7 +140,7 @@ def _wrap_result(data, columns, index_col=None, coerce_float=True,
     frame = DataFrame.from_records(data, columns=columns,
                                    coerce_float=coerce_float)
 
-    _parse_date_columns(frame, parse_dates)
+    frame = _parse_date_columns(frame, parse_dates)
 
     if index_col is not None:
         frame.set_index(index_col, inplace=True)
@@ -818,17 +819,24 @@ def _harmonize_columns(self, parse_dates=None):
         Datetimes should already be converted to np.datetime64 if supported,
         but here we also force conversion if required.
         """
-        # handle non-list entries for parse_dates gracefully
-        if parse_dates is True or parse_dates is None or parse_dates is False:
-            parse_dates = []
-
-        if not hasattr(parse_dates, '__iter__'):
-            parse_dates = [parse_dates]
+        parse_dates = _process_parse_dates_argument(parse_dates)
 
         for sql_col in self.table.columns:
             col_name = sql_col.name
             try:
                 df_col = self.frame[col_name]
+
+                # Handle date parsing upfront; don't try to convert columns
+                # twice
+                if col_name in parse_dates:
+                    try:
+                        fmt = parse_dates[col_name]
+                    except TypeError:
+                        fmt = None
+                    self.frame[col_name] = _handle_date_column(
+                        df_col, format=fmt)
+                    continue
+
                 # the type the dataframe column should have
                 col_type = self._get_dtype(sql_col.type)
 
@@ -846,16 +854,6 @@ def _harmonize_columns(self, parse_dates=None):
                     if col_type is np.dtype('int64') or col_type is bool:
                         self.frame[col_name] = df_col.astype(
                             col_type, copy=False)
-
-                # Handle date parsing
-                if col_name in parse_dates:
-                    try:
-                        fmt = parse_dates[col_name]
-                    except TypeError:
-                        fmt = None
-                    self.frame[col_name] = _handle_date_column(
-                        df_col, format=fmt)
-
             except KeyError:
                 pass  # this column not in results
 
diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index c202fae8c91cf..029ec2de22911 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -28,9 +28,8 @@
 
 from datetime import datetime, date, time
 
-from pandas.core.dtypes.common import (
-    is_object_dtype, is_datetime64_dtype,
-    is_datetime64tz_dtype)
+from pandas.core.dtypes.common import (is_datetime64_dtype,
+                                       is_datetime64tz_dtype)
 from pandas import DataFrame, Series, Index, MultiIndex, isna, concat
 from pandas import date_range, to_datetime, to_timedelta, Timestamp
 import pandas.compat as compat
@@ -1356,9 +1355,7 @@ def check(col):
         # even with the same versions of psycopg2 & sqlalchemy, possibly a
         # Postgrsql server version difference
         col = df.DateColWithTz
-        assert (is_object_dtype(col.dtype) or
-                is_datetime64_dtype(col.dtype) or
-                is_datetime64tz_dtype(col.dtype))
+        assert is_datetime64tz_dtype(col.dtype)
 
         df = pd.read_sql_query("select * from types_test_data",
                                self.conn, parse_dates=['DateColWithTz'])