Multi-disciplinary analysis and optimization (MDAO) has been a long-standing goal in the aerospace community. In order to employ MDAO effectively, one needs to be able to compute the sensitivity of the objective function with respect to the driving parameters in a robust and efficient manner. As models get very large, there is a need to compute these sensitivities in parallel, especially since most optimization methods already employ parallel solvers. Contained herein is a study of two different parallelization strategies for efficiently computing sensitivities. They are compared on a model problem, where the objective is to find the CAD-like design parameters that most-closely match a set of given mass properties. Although quite simple compared with CFD-based optimizations, this model problem allows one to really examine the efficiency and robustness of the parallelization strategies. The results are that for small design changes, a linearized approach to the geometry can be very effective. But for large changes, a non-linear approach, involving rebuilds and computing the sensitivities in parallel with the flow solver, has been found to be the best approach.