This paper presents a novel simulation-based approach for optimizing batter lineups in minor league baseball. Our simulator goes beyond basic hitting statistics by incorporating key factors such as baserunner advancement, player speed, pitcher fatigue, handedness matchups, and situational outcomes like double plays and sacrifice flies. We propose a dynamic model that can simulate the expected runs scored by different lineups. We demonstrate the simulator’s usefulness by comparing real-world lineups, revealing optimal configurations that could significantly impact game outcomes. Our framework offers a valuable tool for coaches and analysts to make informed decisions in the complex and uncertain environment of minor league baseball.