Design optimization is an important engineering design activity. When used during the development of a new product, the overall profitability of that product depends upon the quality of the solution that the optimization model returns as well as the time and cost of using optimization. There exist many different ways to use optimization. The design engineer wants to select the most appropriate optimization model to create a profitable design. This paper discusses this meta-design (or meta-reasoning) problem and presents a method for selecting the best (most profitable) optimization model from a set of candidate optimization models. The approach allows multiple ways to handle uncertainty about the optimization models. We demonstrate the approach by considering the optimization of a universal electric motor.