The invention discloses a multi-step prediction method of parking based on an optimized wavelet neural network. The method comprises a step of processing actually measured effective parking data intoan effective parking time series with a time interval of 5 minutes, and performing multiple-scale decomposition and reconstruction by using a wavelet function 'db32' to, and taking the function as a hidden layer function of the wavelet neural network, a step of adjusting a weight by using a particle swarm algorithm and carrying out gradual iterative update to obtain an optimal value, and a step ofreducing a prediction time of EPWNN by using an ELM algorithm and obtaining a prediction result according to a multi-step prediction strategy. Compared with genetic algorithm optimization neural network, genetic algorithm optimization wavelet neural network, extreme learning machine optimization wavelet transform, extreme learning machine optimization wavelet neural network, particle swarm optimization neural network algorithm, particle swarm optimization wavelet neural network and other algorithms, the prediction error of an EPWNN algorithm is reduced by 89.17%, and the time needed by prediction is reduced by an average of 50.83%.