Aiming to handle the problem of multi-unmanned vehicle task allocation in agricultural product transportation scenarios between production sites (farms) and distribution sites (markets), a novel combinatorial optimization model that incorporated delivery time requirements, vehicle constraints, and task complexity was established at first. Subsequently, an improved fractional order particle swarm optimization (IFOPSO) algorithm was proposed. By introducing a fractional-order Lévy random step size into the particle swarm optimization (PSO) algorithm, the global search capability was significantly enhanced. Additionally, a mechanism for adaptively adjusting the Lévy order was designed to improve the convergence accuracy, robustness, and overall optimization performance of IFOPSO. Experimental results based on ten benchmark functions demonstrated that the proposed IFOPSO algorithm exhibited significant advantages in terms of convergence speed, accuracy, and global search ability compared with existing algorithms. Furthermore, an optimization model for unmanned vehicle pickup and delivery task scheduling was developed, where the total cost accounted for travel cost, time violation cost, load violation cost, and start-up cost. The IFOPSO algorithm was applied to solve this task allocation problem, and comparative experiments with traditional PSO, improved PSO, and fractional order PSO algorithms showed that the proposed algorithm effectively reduced scheduling costs, improved solution efficiency, and rapidly identified a feasible and optimal pickup and delivery solution.