<p>As modern manufacturing increasingly emphasizes production efficiency and flexibility, the scheduling of automated guided vehicles (AGVs) within matrix manufacturing workshops has emerged as a significant area of research. Effectively scheduling AGVs to fulfill production requirements while optimizing multi-objective performance is crucial for addressing production scheduling challenges. To tackle the complexities of AGV scheduling, the paper introduces the dual-criteria collaborative evolutionary algorithm (DCCEA), a sophisticated multi-objective evolutionary algorithm designed to optimize scheduling performance while meeting production demands. The DCCEA employs a dual-criteria heuristic to improve convergence rates and solution quality by prioritizing customers with higher fitness levels, thus starting the search from a robust initial population. It incorporates a multi-strategy co-evolutionary framework that features elite-oriented population selection, a bimodal adaptive crossover operator, and a dynamic window mutation strategy, which collectively enhance exploration in high-dimensional objective spaces while preserving diversity and minimizing premature convergence. Additionally, a local optimizer based on route load is introduced to refine the solution distribution and prevent local optima convergence. Experimental evaluations demonstrate that DCCEA outperforms other eight algorithms. Specifically, compared with the most competitive comparison algorithm, DCCEA achieves a 13.47% improvement in the hypervolume metric and a 53.15% decrease in the inverted generational distance. These results validate the algorithm’s effectiveness in resolving AGV scheduling challenges and achieving both global and local optimization in complex, multi-constraint environments.</p>