Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications.