In this thesis, we investigate the capabilities of Ant Colony Optimization (ACO) metaheuristic to solve combinatorial and multi-objective optimization problems. First, we propose a taxonomy of ACO algorithms proposed in the literature to solve multi-objective problems. Then, we study different pheromonal strategies for the case of mono-objective multidimensional knapsack problem. We propose, finally, a generic ACO algorithm to solve multi-objective problems. This algorithm is parameterised by the number of ant colonies and the number of pheromone structures. This algorithm allows us to evaluate and compare new and existing approaches in the same framework. We compare six variants of this generic algorithm on the multi-objective multidimensional knapsack problem.