This surrogate-based global optimization method is able to improve force field LJ parameters by escaping local minima, leading to chemical insight and improved parameters. The success of the strategy is due to its multi-fidelity approach, using a cheaper surrogate to apply an otherwise prohibitively expensive global optimization algorithm. While already useful in its current form, the flexibility of the framework allows for significant improvement of the strategy in the future. We believe that this technique can help modelers perform better optimizations against physical property data in the future, leading to force fields which more accurately predict the behavior of molecular systems of interest.