Freight-passenger co-modal metro station serve as key nodes in establishing urban underground logistics networks, with logistics spatial design directly impacting the freight capacity of metro networks and station retrofit cost. To address the coordinated configuration of multiple logistics functional areas and operating equipment within stations under cargo-handling demand and limited site resources, this study proposes a logistics space layout optimization method for a freight-passenger co-modal metro station. Based on the structure of an underground double-island station, the grid method is adopted to characterize the site space. A bi-level programming model is developed by considering constraints related to functional area shape, equipment configuration, service capacity, and obstacle-avoiding passage, with the objectives of minimizing construction and equipment costs, reducing handling distance, and improving functional agglomeration. To enhance computational efficiency, a hybrid heuristic algorithm integrating adaptive immune genetic operators and a simulated plant growth mechanism is designed. Numerical experiments show that, compared with the genetic algorithm (GA) and simulated annealing algorithm (SA), the proposed algorithm improves the objective value by 5.13% and 9.47%, respectively, and exhibits good convergence performance. Simulation results for a real metro station indicate that the proposed method reduces the total layout cost by 37.3% compared with the initial scheme, outperforming GA (17.5%) and SA (23.2%). Under the threshold constraint of site circulation capacity, the total logistics space accounts for 74% of the maximum buildable area of the freight station hall. The results can provide methodological support for logistics space design and underground logistics facility allocation in a freight-passenger co-modal metro station.