In this work, a novel approach called multi-objective multi-colony bacterial foraging algorithm for multi-objective optimization (M²BFO) is proposed. The proposed M²BFO extend original bacterial foraging optimization (BFO) algorithm to multi-objective and cooperative mode by combining external archive and cooperative search strategy. Our algorithm uses the concept of Pareto dominance to determine the swim direction of a bacterium and maintains nondominated solution vectors in external archive based on greedy selection and crowing distance strategies. With cooperative search approaches, the single population BFO has been extended to interacting multi-colony model by constructing colony-level interaction topology and information exchange strategies. Simulation experiment of M²BFO on a set of benchmark test functions are compared with other nature inspired techniques which includes nondominated sorting genetic algorithm II (NSGAll) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate M²BFO approach is a powerful search and optimization technique for multi-objective optimization problems.