This paper introduces a novel Nature-inspired Multi-Objective Particle Swarm Optimization algorithm for Multifactorial Problems, called MOPSO-mfact. The MOPSO-mfact algorithm operates in two processing steps: an initial pre-search in a unified search space, followed using a dominance operator to identify the best skill factor. Subsequently, individuals with identical skill factors are grouped into sub-swarms for multitasking optimization, avoiding random mating probabilities. MOPSO-mfact was evaluated using 36 Multifactorial (ETMOF) benchmark suite. Comparative analysis using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics, along with the Mean Standard Score (MSS), determined the best multitasking approach. Parameter optimization was achieved via sensitivity analysis with the Taguchi method. MOPSO-mfact demonstrated promising results, achieving a strong MSS for 28 ETMOF problems and solving 33 out of 36 problems based on MIGD metrics.