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The allocation of merit-based awards and need-based aid is important to both universities and students who wish to attend the universities. Current approaches tend to consider only institution-centric objectives (e.g. enrollment, revenue) and neglect student-centric objectives in their formulations of the problem. There is lack of consideration to the need to improve access and affordability to higher education. Previously, we contributed a metaheuristic and machine learning approach for optimizing strategies that allocate merit-based awards and need-based aid. The approach can be used to optimize both institutioncentric ( e.g. enrollment and revenue) and student-centric objectives (affordability and accessibility to higher education). We now employed an improved version of this approach to explore comprehensively a recent admission dataset from our university. We showed that current applicants depended very much on financial sources other than federal and institution aid to attend the university. This potentially created a financial burden for many of these applicants. We identified seven budget-friendly strategies that promise to increase access to higher education significantly by more than 100%, while still keeping it affordable for students and limiting a budget increase to less than 7%. Additionally, we identified a total of 111 strategies, including those that benefit from more aggressive changes in the budget to obtain higher increases in enrollment, revenue, and/or higher affordability and accessibility for students. This method may be used by other institutions in ways that best fit their institutional objectives and students’ profiles.