Effectively estimating student enrollment and recruiting students is critical to the success of any university. However, despite having an abundance of data and researchers at the forefront of data science, traditional universities are not fully leveraging machine learning and data mining approaches to improve their enrollment management strategies. In this project, we use data at a large, public university to increase their student enrollment. We do this by first predicting the enrollment of admitted first-year, first-time students using a suite of machine learning classifiers (AUROC = 0.85). We then use the results from these machine learning experiments in conjunction with genetic algorithms to optimize scholarship disbursement. We show the effectiveness of this approach using real-world enrollment metrics. Our optimized model was expected to increase enrollment yield by 15.8% over previous disbursement strategies. After deploying the model and confirming student enrollment decisions, the university actually saw a 23.3% increase in enrollment yield. This resulted in millions of dollars in additional annual tuition revenue and a commitment by the university to employ the method in subsequent enrollment cycles. We see this as a successful case study of how educational institutions can more effectively leverage their data. [For the full proceedings, see ED607784.]