Abstract: The spreader is a core material handling component of port cranes. During operation, it is frequently subjected to complex stresses such as impact loads and bending moments induced by heavy-lifting tasks. Prolonged service inevitably leads to fatigue failure, which directly compromises the efficiency and safety of port operations. To achieve a highly reliable design for the key structural parameters of port spreaders, this paper proposes an optimization approach that integrates the Kriging surrogate model with the multi-objective particle swarm optimization （MOPSO） algorithm.Firstly, the Latin hypercube sampling （LHS） method is adopted to conduct structural parameter sampling within predefined ranges for the fatigue-prone regions of the spreader, including the rod body and the fillet at the lifting lug joint. Secondly, a Kriging prediction model is constructed based on the sampling results to realize fatigue life prediction and uncertainty quantification for the key structural parameters of the spreader. Finally, the MOPSO algorithm is utilized to explore the optimal solution space, generating a non-dominated solution set that satisfies the dual objectives of maximizing the predicted fatigue life and minimizing uncertainty. The Pareto frontier is then plotted to screen out the optimal combination of structural parameters. The results verify the reliability and rationality of the proposed approach in the structural design of port spreaders.