In this paper, we propose an evolutionary framework for model fi- delity control that decides, at runtime, the appropriate fidelity level of the compu- tational model, which is deemed to be computationally less expensive, to be used in place of the exact analysis code as the search progresses. Empirical study on an aerodynamic airfoil design problem based on a Memetic Algorithm with Dy- namic Fidelity Model (MA-DFM) demonstrates that improved quality solution and efficiency are obtained over existing evolutionary schemes.