Decision tree learning is widely done heuristically, but advances in the field of optimal decision trees have made them a more prominent subject of research. However, current methods for optimal decision trees tend to overlook the metric of robustness. Our research wants to find out whether the robustness of optimal decision trees can be improved by incorporating randomization. To achieve this, we added randomization to the existing MurTree algorithm, and performed experiments to compare the robustness. The results show that adding randomization improves the robustness of the decision tree but lowers the out of sample accuracy.