To improve the convergence speed and optimization accuracy of the red-billed blue magpie optimizer, a multi-strategy improved red-billed blue magpie optimizer is proposed for the first time. Firstly, in order to improve the diversity and coverage of the initial population, circle chaotic mapping is used to initialize the population; secondly, combining the spiral search strategy with hunting behavior to expand the optimization range, while balancing the algorithm′s global exploration ability and local development ability; finally, the Cauchy mutation perturbation strategy is introduced during the iteration process to avoid the algorithm falling into local optima in the later stages, further improving the overall efficiency of the algorithm. Using 15 test functions for simulation experiments, the results show that the improved algorithm has improved optimization accuracy, convergence speed and stability. Compared to the original algorithm, the average error was reduced by 90.68% and the average standard deviation was reduced by 98.60%. The application of the improved algorithm to engineering optimization problems has verified its feasibility.