Multifidelity optimization leverages the fast run times of low-fidelity models with the accuracy of high-fidelity models, in order to conserve computing resources while still reaching optimal solutions. This work focuses on the multidisciplinary multifidelity optimization of an unmanned aerial system model with finite element analysis and computational fluid dynamics simulations in-the-loop. A two-step process is used where the lower-fidelity models are optimized, and then the optimizer is used as a starting point for the higher-fidelity models. By starting the high-fidelity optimization routine at a nearly optimal section of the design space, the computing resources required for optimization are expected to decrease when using gradient-based algorithms. Results show that, at least in some cases, the multifidelity workflows save time over optimizing the original high fidelity model alone. However, the model management strategy did not find statistically significant differences between the differing optimization approaches when used on this test problem.