Fuzzy C- means clustering algorithm is an iterative hill-climbing technique for the local search algorithm, due to the sensitive dependence on initial conditions and easy to fall into the local minimum. Genetic algorithm is a global optimization algorithm, can overcome the fuzzy C- means clustering algorithm to fall into the local minimum problem, but the genetic algorithm has slow convergence, premature convergence. Application of niche theory on genetic algorithm improvements, design based on shortest distance arithmetic crossover operator, mutation operator, boundary double elite seed in evolutionary strategy, in order to protect the population genetic diversity. The simulation results show that, the improved algorithm can improve the convergence speed of fuzzy clustering and clustering quality.