Abstract: In this article, we propose a meta-heuristic algorithm for solving multi-objective combinatorial optimization problems. The proposed multi-objective combinatorial optimization algorithm is developed by combining the good features of popular guided local search algorithms like simulated annealing (SA) and tabu search (TS). It has been organized as a multiple start algorithm to maintain a good balance between intensification and diversification. The proposed meta-heuristic algorithm is evaluated by solving the stacking sequence optimization of hybrid fiber-reinforced composite plate, cylindrical shell, and pressure vessel problems. The standard performance metrics for evaluating multi-objective optimization algorithms are used to demonstrate the effectiveness of the proposed algorithm over other popular evolutionary algorithms like Nondominated Sorting Genetic Algorithms (NSGA-II), Pareto Archived Evolutionary Strategy (PAES), micro-GA, and Multi-Objective Particle Swarm Optimization (MOPSO).