In the highest-accuracy mixture models available today, these being the multi-fluid Helmholtz-energy explicit formulations, there are a number of binary interaction parameters that must be obtained through correlation or estimation schemes. These binary interaction parameters are used to shape the thermodynamic surface and yield higher fidelity predictions of various thermodynamic properties including vapor-liquid equilibria and homogeneous p-v-T data, among others. In this work, we have used a novel and entirely automatic evolutionary optimization algorithm written in the python programming language to fit the two most important interaction parameters for more than 1100 binary mixtures. This fitting algorithm can be run on multiple processors in parallel, resulting in a reasonable total running time for this large set of binary mixtures. For more than 830 of the binary pairs, the median absolute relative error in bubble-point pressure is less than 5%. The source code for the fitter is provided as supplemental data, as well as the entire set of binary interaction parameters obtained and comparisons with the best experimental vapor-liquid-equilibrium data that are available.