Harmony Search (HS) is an emerging meta-heuristic optimization method and has been used to tackle various optimization problems successfully. However, the research of multi-objectives HS just begins and no work on binary multi-objectives HS has been reported. This paper presents a multi-objective binary harmony search algorithm (MBHS) for tackling binary-coded multi-objective optimization problems. A modified pitch adjustment operator is used to improve the search ability of MBHS. In addition, the non-dominated sorting based crowding distance is adopted to evaluate the solution and update the harmony memory to maintain the diversity of algorithm. Finally the performance of the proposed MBHS was compared with NSGA-II on multi-objective benchmark functions. The experimental results show that MBHS outperform NSGA-II in terms of the convergence metric and the diversity metric.