Concerning the defects of low optimization precision and easy to fall into local optimum in Fruit Fly Optimization Algorithm (FOA), a Fruit Fly Optimization Algorithm based on Simulated Annealing (SA-FOA) was proposed. The receiving mechanism of solution and the optimal step size were improved in SA-FOA. The receiving probability was based on the generalized Gibbs distribution and the receiving of solution met Metropolis criterion. The step length decreased with the increasing iteration according to non-uniform variation idea. The simulation result using several typical test functions show that the improved algorithm has high capability of global searching. Meanwhile, the optimization accuracy and convergence rate are also improved greatly. Therefore, it can be used to optimize the parameters of neural network and service scheduling models.