Efficient media scheduling plays a vital role in advertising campaign success. Among media platforms, television remains a key channel for audience engagement. However, allocating advertisements across programs, times, and channels is a complex task. The number of possible combinations exceeds human evaluation capacity. Advertisers often face challenges in allocating spots effectively while minimizing plan cost. This study implements a Mixed Integer Linear Programming (MILP) model to optimize spot allocation across TV programs. The objective is to minimize the Cost Per Rating Point (CPRP), a key efficiency metric. The model maximizes the normalized gross rating point (GRP), subject to constraints such as total budget, program slot availability, and fair commercial distribution. By transforming a nonlinear objective into a solvable MILP framework, the model ensures practical feasibility and scalability. The results demonstrate improved CPRP performance and significant time savings under realistic constraints. This approach supports advertisers in making data-driven, cost-effective media scheduling decisions. The study offers a replicable framework for optimizing television advertising plans in dynamic markets.