To solve the problems of premature convergence and easy falling into local optimum in the differential evolution algorithm when dealing with function optimization problems of population diversity, we propose a cooperative differential evolution algorithm with elitistpopulation strategy (ESCDE). In the population optimization process, we firstly evaluate population fitness,  extract the first several excellent individuals as the elite population and divide the others randomly into three sub-populations of the same size. Each subpopulation adopts a different evolution strategy to guarantee the diversity of the population and improve the performance of the algorithm. And then, the elite population and the remaining three subpopulations are updated according to the new fitness value to avoid falling into local optimum. Finally, comparison are made between the proposed algorithm and four other advanced differential evolution algorithms on 30 standard test functions in CEC2014. Experimental results indicate that the proposed algorithm can improve convergence speed effectively with higher convergence precision and better optimization performance.