Abstract: Granule number and classification error rate are two conflicting objectives in granular computing, it is impossible to minimize the two objectives simultaneously. The multi-objective optimization including the number of granule number and classification error was formed and solved by multi-objective evolutionary algorithm, and a series of multihyperbox granule sets were achieved. The multiobjective evolutionary algorithm obtained the different solution set by initialization of population, evolution operation and iteration method. Users can select the solution according to their requirements.