A shuffled frog leaping algorithm is proposed to overcome the defects of fall into local optimum easily and the lack of ability to solve discrete optimization problems when solving high-dimensional complex problems. In the proposed algorithm, using the perturbation coefficient to regulate the movement of individual frog distance, so as to better balance the global search and local development capabilities of the algorithm; using the spiral update position strategy to enable the algorithm to perform a more comprehensive and refined search near the optimal solution; using a random search strategy to improve the global search ability of the algorithm; using the 2-opt method to implement the global optimal solution mutation to increase the diversity of the population; the SFLA algorithm is discretized by the improved Sigmoid function. The optimization experiments are conducted on the 9 benchmark functions and oilfield measures planning. Simulation results show that the proposed DSFLA has a better search performance.