It is necessary to develop some novel multi-objective optimization algorithms to cope with the complicated multi-objective optimization problems which are emerging and increasingly hard in reality. The basic firefly algorithm is extended to the realm of multi-objective optimization, and a hybrid multi-objective firefly algorithm (HMOFA) is proposed in this paper. Firstly, an initialization approach of mix-level orthogonal experimental design with the quantification of the continuous search space is used to generate an even-distributed initial population in the decision space. Secondly, the elites in the external archive are randomly selected to guide the movement of the fireflies in the evolutionary process. Finally, the archive pruning strategy based on three-point shortest path is used to maintain the diversity of the external archive. The proposed HMOFA is compared with other five peer algorithms in the performance of hypervolume based on seventeen benchmark multi-objective test instances, and the experimental results show that the HMOFA employs the overall performance advantages in convergence, diversity and robustness over other peer algorithms.