Today, shape optimization is one of the areas that is focused on research and development in industries. Thanks to the strength of computer technology, the shape simulation and optimization model could be analyzed quickly, robustly and exactly. Such processes have generally two major ingredients a suitable parameterization of the geometry to be optimized and an optimization algorithm. There are many ways to accomplish this process, one of which is the modern optimization method by coupling computational fluid dynamics (CFD) and optimization algorithm. At the same time, it is necessary to define the objective function, the design variable and the algorithm for the optimal phase. In this paper, a method of optimizing geometry by combining CFD and evolution algorithms (EA) is presented with the goal of reducing the drag coefficient. The initial geometry was built by a list of control points, and they are connected by BSpline curve. The control points are moved automatically through the EA method by Dakota (Design and Analysis toolKit for Optimization and Terascale Applications) software. The control points are adjusted their positions through the iterative loop in order to achieve a better result meet the objective function. Using this methodology, we finally find a new geometry has a smaller drag coefficient than the initial geometry.