In this work we tested different configuration settings for the NMPSO algorithm, aiming at solving multi-objective optimization problems with a small number of function evaluations, which is an important aspect that must be addressed in real-world optimization problems. Sixteen different configurations were tested for NMPSO, with different combinations of: i) the swarm size, ii) the maximum number of particles in the external archive, and iii) the maximum amount of genetic operations in the external archive. Three DTLZ problems were used to select the best configuration, which was then evaluated against other state-of-the-art multi-objective optimization algorithms (MMOPSO, NSGA-II, NSGA-III). Our results showed that the fastest convergence towards the true Pareto-optimal front is provided by the configuration with a swarm size of 10, a maximum number of particles allowed in the external archive of 100, and a limit of genetic operations per iteration given by 50% of the maximum number of particles allowed in the external archive. The selected configuration was also very competitive or even superior against NSGA-II and NSGA-III, in terms of the number of function evaluations required to start having an HV larger than zero, but also in the HV values achieved after stabilization of the Pareto-optimal front.