This research presents an approach to the automatic generation of electro-mechanical engineering designs. Our approach is to apply Messy Genetic Algorithm optimization techniques to the evolution of assemblies composed of Lego elements. Each design is represented as a labeled assembly graph. Designs are evaluated based on a set of behavior and structural equations, which we are trying to optimize. Our eventual goal is to introduce simulation of electro-mechanical devices into our evaluation functions. Initial populations are generated at random, with design candidates for subsequent generations produced by a user-specified selection technique. Crossovers are applied by using cut and splice operators at random points of the chromosomes; random mutations are applied with a specified low probability to modify the graph. This cycle continues until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies (Legos), as well as the creation of selection criteria for this domain. We believe that this research creates a foundation for future work and can be used to apply GA techniques to the evolution of more complex and realistic electro-mechanical structures.