In a typical discrete manufacturing process, a new type of reconfigurable production line is introduced, which aims to help small- and mid-size enterprises to improve machine utilization and reduce production cost. In order to effectively handle the production scheduling problem for the manufacturing system, an improved multi-objective particle swarm optimization algorithm based on Brownian motion (MOPSO-BM) is proposed. Since the existing MOPSO algorithms are easily stuck in the lo-cal optimum, the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM. To further strengthen the global search capacity, a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function (GCDF) is included, which helps to maintain an excellent convergence rate of the algorithm. Based on the commonly used indicators generational distance (GD) and hypervolume (HV), we compare the MOPSO-BM with several other latest algorithms on the benchmark functions, and it shows a better overall performance. Furthermore, for a real reconfigurable production line of smart home appliances, three algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II), decomposition-based MOPSO (dMOPSO) and MOPSO-BM, are applied to tackle the scheduling problem. It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.