This paper addresses subsonic airfoil optimization using Evolution Strategies (ES) and devises a means of defining the airfoil geometry that reduces unnecessary restrictions in the search space. The solution encoding uses Bezier Control Points to define the geometry of the airfoil, but does not restrict the movement of the control points as was common in previous airfoil optimization algorithms. The ES move operator combined with this improved solution encoding expands the search space to include superior solutions while also enabling a more efficient search to reduce computational cost.