Hardware-in-the-loop (HIL) modeling is a powerful way of modeling complicated systems. However, some hardware is expensive to use in terms of time or mechanical wear. In cases like these, optimizing using the hardware can be prohibitively expensive because of the number of calls to the hardware that are needed. Variable fidelity optimization can help overcome these problems. Variable fidelity optimization uses less expensive surrogates to optimize an expensive system while calling it fewer times. The surrogates are usually created from performing a design of experiments on the expensive model and fitting a surface to the results. However, some systems are too expensive to create a surrogate from. One such case is that of a flapping flight model. In this thesis, a technique for variable fidelity optimization of HIL has been created that optimizes a system while calling it as few times as possible. This technique is referred to as an intelligent DOE. This intelligent DOE was tested using simple models of various dimension. It was then used to find a flapping wing trajectory that maximizes lift. Through testing, the intelligent DOE was shown to be able to optimize expensive systems with fewer calls than traditional variable fidelity optimization would have needed. Savings as high as 97% were recorded. It was noted that as the number of design variables increased, the intelligent DOE became more effective by comparison because the number of calls needed by a traditional DOE based variable fidelity optimization increased faster than linearly, where the number of hardware calls for the intelligent increased linearly.