An improved slime mold algorithm with artificial bee colony (ISMABC) was proposed aiming at the problem of cooperative path planning for multiple unmanned aerial vehicles (UAVs). A path planning cost model was established, and the path planning problem in a three-dimensional environment was transformed into an optimization problem by introducing a fitness function and constraint conditions, which was solved by the proposed algorithm to obtain the optimal path. The slime mold algorithm was improved by employing the good point set strategy and a nonlinear convergence factor. Then the population diversity was increased, and the convergence speed of the algorithm was accelerated. An elite opposition-based learning strategy was applied to the global best individual in order to enhance population quality. A global best position guidance was introduced based on the exploration capability of the artificial bee colony in order to improve the exploitation capability of the slime mold algorithm. Comparative analysis of optimization on 14 test functions and some functions from the CEC2017 test suite showed that the optimization ability and convergence speed of the ISMABC algorithm were significantly enhanced. The algorithm was applied to solve the problem of cooperative path planning for multiple UAVs in order to verify the feasibility of the ISMABC algorithm. Comparative analysis shows that the ISMABC algorithm can be used to plan paths with minimal cost that satisfies the constraints for each UAV.