According to the strong exploitation and poor exploration abilities of gravitational search algorithm(GSA), an niching behavior based advanced GSA (NAGSA) is proposed. After analyzing the performance of GSA, NAGSA defines the mass affinity and Euclidean-distance affinity for each particle, and then each particle affinity probability is calculated according to these two attributes instead of the original sorting mass method. The use of affinity probability and crow ding niching behavior guides each particle to search in its neighboring field, thus NAGSA can make a balance between convergence rate and diversity maintaining. Besides, the value of k-best decreases according to exponential function, so that the convergence accuracy is improved. Simulations on ten benchmark functions indicate that NAGSA can improve the accuracy of optimum effectively and accelerate the convergence rate apparently. Furthermore, the algorithm is proved to be feasible and advantageous in the simulation of four standard flexible job shop scheduling modules.