The path planning problem for mobile robots involves a large number of nodes and a wide search space. It also considers factors such as safety and real-time requirements. To address the multi-objective path planning problem for mobile robots, this study proposes a novel multi-objective intelligent optimization algorithm that combines reinforcement learning. Firstly, the algorithm adopts NSGA-II as the base framework and equips individuals with learning capabilities by reinforcement learning. A SARSA operator is designed to improve the global search efficiency of the algorithm. Secondly, to accelerate the convergence speed and ensure population diversity, the study introduces an adaptive simulated binary crossover operator (tanh-SBX) as an auxiliary operator and divides the population into two sub-populations with different properties: elite and non-elite populations. Finally, the study designs four different strategies and calculates the probability of updating strategies using the Metropolis criterion of the simulated annealing algorithm. It allows the most suitable strategy to guide the population’s optimization direction, balancing exploration and exploitation. Simulation experiments demonstrate that the proposed algorithm can find optimal paths in environments with different complexities. Compared to traditional intelligent biomimetic algorithms, the proposed algorithm effectively balances optimization objectives and discovers safer and better paths in more complex environments.