This paper investigates the multi-objective scheduling problem of intelligent unmanned operations and analyzes the interdependent constraints among the various links of the operation workflow. To address the coupled challenges of job sequencing and resource allocation inherent in unmanned operation scenarios, an integrated scheduling model based on a two-layer particle swarm optimization framework is proposed. A mixed-integer programming formulation is adopted to rigorously characterize the structural constraints and logical dependencies within the scheduling process. Building upon this model, an enhanced two-layer particle swarm optimization algorithm with fragment-based particle encoding is introduced to expand the feasible search space. Moreover, a dynamic inertia weight adjustment mechanism and an adaptive mutation operator are incorporated to strengthen the algorithm’s global exploration capability while accelerating convergence. Simulation experiments verify that the proposed model and algorithm effectively optimize the scheduling of unmanned operations under complex operational constraints, significantly improving system-level support performance. These results demonstrate that the method provides a robust and efficient solution for multi-objective intelligent scheduling tasks in highly constrained unmanned operation environments.