Because the parameter optimization algorithm of support vector machine (SVM) has many control parameters and fall into the local optimum easily, and in order to realize diagnosis, rolling bearings fault diagnosis of SVM optimized by surface-simplex swarm evolution algorithm is proposed. First, surface-simplex swarm evolution (SSSE) establishes the particle's simple neighborhood search operator in a random way to reduce control parameters and the multi-role state search strategy to avoid falling into the local optimum. Then SSSE is applied to parameter optimization of SVM to realize fault diagnosis. In the test，the energy matrix of ensemble empirical mode decomposition(EEMD) of rolling bearing signal is used as the feature input to perform performance analysis and testing of the algorithm. The results show that the method not only effectively avoid the particles falling into the local optimal solution，but also reduce the control parameters, and can complete the signal diagnosis.