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Tourists’ spatial-temporal behavior has complex and dynamic randomness, which affects the intelligent management of scenic spots. Understanding and mastering the spatial-temporal behavior of tourists within the peak period of scenic spots is one of the key means to improving the competitiveness of scenic spots and enhancing tourism satisfaction. This paper explores how to effectively optimize the tourists’ spatial-temporal behavior and improve the overall efficiency of scenic spots. Firstly, we built a simulation model and found the transfer change law between attractions through historical data. Then, we predicted the spatial-temporal distribution of the number of tourists to their subsequent attractions and selected the minimum load attraction as the next destination for tourists. Finally, the simulation experiment proves that the dynamic selection optimization based on individual tourist behavior can balance the scenic spot load and alleviate the congestion level.