Wave Energy Converters (WECs) promise to be a viable alternative to current electrical generation methods. However, these WECs must become more efficient before wide-scale industrial use can become cost-effective. The efficiency of a WEC is primarily dependent upon its geometry and ballast configuration which are both difficult to evaluate, due to slow computation time and high computation cost of current models. In this thesis, we use evolutionary algorithms to optimize the ballast geometry of a wave energy generator using a two step process. First, we generate a function approximator (neural network) to predict wave energy converter power output with respect to key geometric design variables. This is a critical step as the computation time of using a full model (e.g., AQWA) to predict energy output prohibits the use of an evolutionary algorithm for design optimization. The function approximator reduced the computation time by over 99% while having an average error of only 3.5%. The evolutionary algorithm optimized the weight distribution of a WEC, resulting in an 84% improvement in power output over a ballast-free WEC.