Wafer manufacturing involves multi-module coordination and strong temporal constraints. Traditional scheduling methods struggle in high-mix production scenarios due to poor adaptability and difficulty in handling complex constraints. To address the dynamic scheduling challenges under strict Just-in-Time (JIT) constraints, this paper proposes an efficient dynamic scheduling scheme named GA-JIT Scheduler, based on a genetic algorithm. The approach models equipment and processes using directed graphs, encoding JIT and other complex constraints into a fitness function. By integrating time-window detection and genetic evolution strategies, a "perception-decision-execution" closed-loop tuning mechanism is constructed to enable rapid responses to dynamic disturbances. This paper verifies the GA-JIT Scheduler using 4 differentiated scheduling tasks from the "9th National Innovation Competition (BeiFang HuaChuang Cup)", with the measured solution times of the four tasks being 93256.5s, 15311.5s, 13013.5s, and 18470s respectively. The algorithm satisfies constraints such as equipment exclusivity and JIT (movement ≤ 30s, residence time ≤ 15s), adapts to multiple scenarios, verifies its engineering applicability and scalability in the dynamic scheduling of wafer manufacturing under strict JIT constraints, and provides a feasible solution for wafer manufacturing with high mix and strong temporal constraints.