Aerospace applications often require complex flow simulations that cannot be computed quickly. To determine the optimum configuration or design in these applications can require the tuning of numerous parameters, the effectiveness of which can only be tested through repeated computational fluid dynamic simulations. Depending on the degree of the flow complexity, systematic searches of the multi-dimensional parameter space is often too costly in time, materials, and computational power. A viable alternative is evolutionary search algorithms which can look for regions of high performance with many fewer configuration evaluations. However, search algorithms can also prove computationally expensive when the evaluation requires relatively costly evaluations. This paper investigates means to reduce the cost of these searches while applying a genetic algortihm approach to two test problems: steady multijet flow control and sensor placement on an atmospheric probe. The objective is to build on previous research to generate a more efficient approach to design optimization in these types of applications.