In building material equipment group enterprises (BME-GEs), unstable manufacturing quality and insufficient delivery capacity are the common problems, where their on-time delivery capacity is greatly affected by manufacturing quality. Therefore, improving manufacturing quality control ability is the key approach to improve their core competitiveness. The manufacturing quality of a product is determined by the attributes of multiple manufacturing resources, therefore, optimizing the utilization of manufacturing resources can improve product manufacturing quality and reduce quality problems in BME-GEs, so as to reduce delivery delay caused by quality problems. Aiming at achieving the goal mentioned above, we were inspired by biological genetic engineering, and a product gene recombination mechanism was analyzed and designed based on our existing studies on product gene. And a multi-objective scheduling model considering minimizing total costs and makespan while maximizing quality for the distributed collaborative manufacturing mode of BME-GEs was established, where a comprehensive measurement method of quality according to our research on product gene evaluation method was presented. To deal with the multi-objective problem, the Fuzzy Analytic Hierarchy Process (FAHP) was applied to determine the weight of each objective. To solve the above model, a hybrid genetic algorithm and discrete particle swarm optimization (H-GA-DPSO) based on multi-level encoding method was designed, which optimized the operation-sequencing, job-to-cell assignment and operation-to-machine assignment by considering the frequent operation outsourcing in BME-GEs. To verify the performance of the above model and algorithm, several comparison experiments including tabu search (TS), GA, DPSO and H-GA-DPSO were presented. The results indicate that the proposed model and algorithm can effectively solve the optimization problem of manufacturing resources in BME-GEs, and compared with the three single algorithms, the hybrid algorithm has significant advantages on optimization ability as well as comprehensive performance for solving the problem studied in this research.