This study proposes a new multi-objective version of the Search Group Algorithm (SGA) called the Multi-Objective Search Group Algorithm (MOSGA). The MOSGA is the combination of the conventional SGA integrated with an elitist non-dominated sorting technique, enabling it to define Pareto optimal solutions via mutation, offspring generation, and selection. The Pareto archive with a selection mechanism is used to preserve and enhance the convergence and diversity of solutions. The MOSGA is validated on twenty-five prominent case studies, including nineteen unconstrained multi-objective benchmark problems, six constrained multi-objective benchmark problems, and five multi-objective engineering design problems to validate its capability and effectiveness. The statistical results are compared to the outcomes of other well-regarded algorithms using the same performance metrics. The comparative results show that MOGSA is robust and superior in handling a wide variety of multi-objective problems. © 2022