The bin packing problem is a problem where goods with different volumes and dimensions are put into a container so that the volume of goods inserted is maximized. The problem of multi-objective bin packing is a problem that is more commonly found in everyday life, because what is considered in packing is usually not only volume.In this research, a multi-objective genetic algorithm is proposed to solve the multi-objective bin packing problem. The proposed genetic algorithm uses non-dominated sorting and crowding distance methods to get the best solution for each objective and to avoid bias. The algorithm is then tested with several test classes that represent different combinations of item and container sizes.From the results of the tests carried out, it was found that the proposed algorithm can find several solutions which are the best candidate solutions for each objective. Also found how the correlation of each objective in the population
