Abstract: To seek the optimal value for hydrological frequency parameters, and then to obtain a higher precision of hydrological characteristics value, an optimization algorithm of hydrologic frequency parameter based on particle swarm optimization (PSO) and adaptive genetic algorithm (AGA) was proposed. Based on the rule of minimum sum of squared residuals, the rule of the least sum of deviation absolute value and the rule of relative deviation minimum sum of squared residuals, the algorithm was constructed which was applied to hydrological frequency parameter optimization model. Adaptive genetic operators in particle swarm optimization algorithm was introduced, by combining the global search ability of genetic algorithm with quicker convergence rate of particle swarm algorithm effectively, and adaptively, and the crossover and mutation probability was improved, thereby a set of adaptive hybrid algorithm was formed, the optimum parameters of the hydrologic frequency was obtained through the model. By using a municipal meteorological center of rainfall data as an example, the algorithm was compared with other conventional methods in this paper. Results show that the fitting precision and fitness effect of the parameter estimation using the algorithm are superior to conventional methods, and the algorithm provides reference for hydrologic frequency analysis field.