The transportation industry is a major contributor to greenhouse gas emissions. The carbon footprint of diesel school buses in the United States is being overlooked despite their significant portion as public transportation. Sense of an urgent need for school bus electrification raises the importance of the effective and efficient operation of school electric buses. However, capital investment, battery range anxiety, charging strategy, and riding time remain significant obstacles to implementing this solution.To address these issues, we have developed a mathematical optimization model that considers the practical constraints of school bus operation and employs a weighted multi-objective function. The model is non-linear but has been linearized and optimized through pre-processing steps, resulting in significant improvements in solver capability. Our real-world case study implementation of this model saved 56% of travel distance and reduced maximum riding time by half. Additionally, we found that the use of heterogeneous fleets was more economical than homogeneous ones.As network modeling is an NP-hard problem, further research could focus on developing heuristics or metaheuristic algorithms to optimize this approach for large-scale networks. Our work highlights the urgent need for sustainable transportation solutions and offers a promising framework for addressing the challenges of electrifying school bus fleets.