When using optimization algorithms to solve optimization problems, keeping the diversity of population and accelerating the convergence rate of the population can improve the performance of an algorithm.To overcome the main drawbacks of the shuffled frog leaping algorithm which may be easy to get stuck and premature convergence in a local optimal solution, this paper proposes a novel differential shuffled frog leaping algorithm.The algorithm is based on the idea of mutation crossover in differential evolution.In the earlier, it uses beneficial information of the other individuals in sub-group to update the worst individual, which increases the local disturbance and the diversity of population;in the later, the algorithm uses the best individual information to conduct the mutation and cross operation for speeding up the convergence rate of the population.Moreover, this paper uses the archive to keep the diversity of population.The experimental results show that the proposed algorithm is superior to the basic frog leaping algorithm and the average frog leaping algorithm in solving optimization problems.