Abstract: During the reentrant multi-purpose batch process with multiple equipment interactions, blockage, preemption and deadlocks occur frequently, leading to significant challenges in collaborative production and high cost of changing production types. Relying solely on intelligent optimization algorithms often results in severe difficulties at both the encoding and decoding stages and fails to provide detailed and feasible scheduling plans. To address these issues, an intelligent optimization framework integrating heuristic rules and high-fidelity simulation is proposed with the objective of minimizing the makespan. Specifically, a simulation execution framework for production systems is established based on production logic, incorporating three rules including push-pull combination, heavy phase blending, and reentrant priority, to ensure efficient and continuous production. Furthermore, equipment allocation rules under various order combinations are explored using the genetic algorithm to guarantee high-quality decoding. Finally, a genetic algorithm incorporating hybrid initialization and fully reachable mutation is designed to optimize batch allocation and production sequencing. Experimental results on 1000 sets of orders across 28 product types demonstrate that the proposed optimization framework surpasses high-fidelity simulation-based optimization methods based on industrial simulation platforms in terms of stability, effectiveness, and computational efficiency.