To enhance the efficiency of automated track fastener maintenance, a hybrid multi-objective optimization algorithm based on an improved multi-objective particle swarm optimization (MOPSO) enhanced with genetic algorithm (GA) mechanisms was proposed, which is applied to task allocation in automated railway track fastener maintenance robots (ARTFMR). First, a multi-objective optimization model was constructed according to practical operational requirements. Subsequently, a one-dimensional split-point encoding scheme with right-overflow compensation was introduced to compress the search space and ensure operational feasibility of solutions. Two types of seed-based initialization strategies were employed to cover both extremities and the central region of the Pareto front in the initial population, while an external archive was utilized to bootstrap the leader repository, thereby improving the quality of initial solutions. Finally, within the main MOPSO loop, periodic GA-inspired crossover and mutation operations were triggered to enhance exploration in sparse and boundary regions, thus mitigating premature convergence. Simulation results demonstrate that the proposed algorithm achieves excellent performance in both task balancing and operational efficiency, making it well-suited for multi-robot task allocation in ARTFMR systems.