This paper describes a systematic shape tuning procedure of adaptive structures for MEMS actuator applications. Due to fabrication process variations, MEMS devices can have different shapes with varied deflections. Such shape variations should be corrected for specific applications. As a result, it is necessary to establish a shape tuning procedure. Finite element modeling and optimization approach were used to minimize the shape variations. The procedure integrated Python programming, ABAQUS, and optimization algorithm for engineering applications. It used the powerful Python scripts programming, the vast library of ABAQUS functions, and a robust preexisting optimization algorithm, NLPQL, which provides more efficient, flexible, and systematic tools for optimization problems. Optimization was used in the adaptive structural designs and the shape tuning procedure after the assembly. Using this approach, three bimorph, gold-on-polysilicon, samples with different initial shapes were studied for shape tuning. The shape was characterized by maximum tip deflection resulting from thermo-mechanical deformations. The standard deviation of the shape variations was reduced from 1.21 to 0.05 μm after tuning. This reduction was verified by experimental data. Another case with ten devices was studied to confirm the effectiveness of the procedure. The standard deviation of the deflections was reduced from 0.81 to 0.02 μm after tuning. These results demonstrated the effectiveness of the optimum procedure for shape tuning. This general-purpose systematic methodology can be applied to adaptive structures for a variety of aerospace applications.