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Traditional vehicle routing problem can not effectively sovle the in time online and random chaning custeromer demands and the slow convergence rate, in order to conquer the defects, a design method based on monte-carlo and importance sample cross entroy is proposed. Firstly, the vehicle routing problem is modeled mathematically, then the prnciples of monte-carlo and cross entropy algorithm for estimating the spare event appearance probability is described, the cross entropy is improved by add Tsallis entropy, using the monte carlo to estimate the total fee for the path as the real fee, the cross entropy is used to renew the imporatance probability function and partition position to improve the probality of important sample to realize the opitimal path. Finally, the random dynamic route planning method based on Tsallis entropy is defined. The simulation is implemented in the MATLAB, the simlulation result shows the method in this paper can solve the vehicle routing problem effectively, compared with the other methods, it has the rapid convergence rate and convergence precision, therefore, it is an effective random dynamic vehicle routing method with big priority.