Abstract: Combining the parallel searching structure of genetic algorithms with the probabilistic jumping property of simulated annealing, a GASA hybrid strategy is proposed for the weights learning of BP networks to speed up training process, improve starting solutions robustness and generalization ability and overcome local minimum.In this new algorithm, the GA population is initialized with an improved BP algorithm.the comprehensive error index of the training error and the testing error is adopted to be the optimization objective function of BP networks, and the strategy of alternately learning between the training sample sets and the testing sample sets in GA is introduced.The application of BP networks based on the GASA hybrid strategy to measuring and calculating base land price is studied, and the results are compared with the conventional method(regression model).The results show that the GASA hybrid strategy can efficiently avoid BP algorithm converging to local optima and the network simple objective learning easily leading to the phenomenon of over fitting.