Due to differences in resource endowments and industrial policies among different regions， the role of distributed production in improving the competitiveness of manufacturing enterprises is very important. How to use distributed production to enhance the flexibility of mass customization is an important problem to be solved to boost consumer confidence. Combined with the idea of minicells — small manufacturing cells， in the distributed mixed production scenario with the multi-market and multi-product characteristics， an integrated model of distributed factory construction and production scheduling was proposed with the objectives to minimize the operating costs （e.g.， labor and transportation costs） and minimize the makespan. By the proposed model， the minicell construction， worker and machine configuration， as well as production strategies for each batch of products were able to be solved. With the help of the proposed model， the enterprises were able to realize the quick release of production capacity and reasonable mixed flow production， so as to realize distributed manufacturing and sales that meet the multi-region， multi-product， and differentiated needs， and reduce the operating cost in the manufacturing process while guaranteeing the throughput. In addition， a Multi-Objective Particle Swarm Optimization （MOPSO） algorithm was designed to solve the proposed model， and was compared with Non-Dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ） and Multi-Objective Simulated Annealing （MOSA） algorithm. The results of extensive numerical experiments show that MOPSO algorithm outperforms NSGA-Ⅱ and MOSA algorithm with the same running time in terms of three metrics： C-Metric （CM）， Mean Ideal Distance （MID） and Maximum Spread （MS）. The proposed algorithm can provide a high-quality decision-making scheme of production operation for the miniaturized distributed production system.