The traditional search strategy of artificial bee colony algorithm exist some disadvantages when solving complex functions with high dimensions, such as the convergence speed is not fast enough, easy to fall into local optimum. In order to solve this problem, multi-search strategy of artificial bee colony algorithm based on symbolic function is presented. In this algorithm, the new algorithm uses the symbolic function to fuse several different search strategies, give full play to the advantages of the search strategies during evolution, and select the best solution based on the objective function value. The new algorithm can balance the ability of local and global search. Experiments were conducted on a set of 16 benchmark functions, and the results demonstrate that the new algorithm has fast convergence and high accuracy than several other ABC-based algorithms.