Formula 1 race strategy optimization is a highly sophisticated field that requires balancing tire performance, complex degradation profiles, and operational constraints under high-stakes uncertainty. This thesis develops a two-stage stochastic programming approach to determine optimal tire compound selection and pit stop timing, with the objective of minimizing total race time. The research establishes a structured optimization framework that accounts for both deterministic circuit parameters and stochastic variables representing real-world race conditions. A comprehensive sensitivity analysis was conducted to calibrate the model’s coefficients, ensuring that its strategic output reflects the tactical realities observed in recent Formula 1 seasons. The current study utilizes this model to analyze the strategic decision-making process across the 25-race calendar, identifying the interdependencies between lap duration, total race distance, and pit lane time loss.