This study proposes an Improved Particle Swarm Optimization with Genetic OPerators (IPSO-GOP) to determine the optimal trajectory of mobile robots in a complex environment. Firstly, we improve the Particle Swarm Optimization (PSO) and adaptively adjust the inertia weight during the algorithm operation to facilitate the particle search. Besides, we disturb the particles with the chaotic variables to increase the convergence speed. Secondly, we introduce the Genetic OPerators (GOP), i.e., multi-crossover and mutation inherited by the genetic algorithm, to optimize the improved PSO (IPSO), thus getting rid of the local minimum and promoting the population diversity. Finally, the shortest continuous geometric path without collisions is obtained after cubic spline interpolation smooths the path generated by the proposed algorithm. In addition, the proposed algorithm in a multi-obstacle environment circumvents the local optimum and accelerates the convergence. Compared with the PSO, it has significant optimization and advantages in path planning.