A heuristic optimization methodology based on a Genetic Algorithm is presented with the goal to help researchers decide on the optimal set of thermodynamic data and models to use to accurately model phase diagrams and their associated uncertainty. This approach accounts for the errors associated with reported data and how reliable the researcher believes the model to be. Additionally, the results of the Genetic Algorithm provides guidance as to which experiments are needed to enhance the reliability of the dataset and is ideally suited for parameter optimization and sensitivity analysis. Applications include the UO2-PuO2 and UO2-BeO systems.