As the Defense Acquisition Executive (DAE), the Under Secretary of Defense for Acquisitions, Technology and Logistics has full responsibility for supervising the performance of the DoD Acquisition System. A challenge to the DAE is in determining the most efficient allocation of funding in procuring of over eighty Major Defense Acquisition Programs. This thesis develops six different cost functions based on the Unit Theory learning curve model for estimating the cost of each of these MDAP systems. The most suitable of these adds an annual overhead component to the cost modeled by the learning effect. This function is implemented in an integer%linear optimization model, the Procurement Scheduling Optimization Model (PSOM). PSOM allows the planner to specif%: an annual budget limit; demand quantities for each system for all years in the planning horizon; minimum and maximum annual production rates; earliest and latest full rate production (FRP) start periods; and low rate initial production (LRIP) costs and quantities. PSOM determines the minimum cost procurement schedule given these constraints, finding the optimal quantity of each system to be procured each year of the planning horizon.