We analyze the effect of different genetic encodings used for evolving three-dimensional agents with physical morphologies. The complex phenotypes used in such systems often require nontrivial encodings. Different encodings used in Framsticks—a system for evolving three-dimensional agents—are presented. These include a low-level direct mapping and two higher-level encodings: one recurrent and one developmental. Quantitative results are presented from three simple optimization tasks (passive height, active height, and locomotion speed). The low-level encoding produced solutions of lower fitness than the two higher-level encodings under similar conditions. Results from recurrent and developmental encodings had similar fitness values but displayed qualitative differences. Desirable advantages and some drawbacks of more complex encodings are established.