This study addresses the multi-constraint optimization problem of photovoltaic-storage irrigation pump trucks, which arises from the nonlinear coupling among photovoltaic power fluctuations, energy storage operational constraints, and the hydraulic characteristics of water pumps. A dynamic historical particle swarm optimization (DHPSO) algorithm was developed, and a multi-time-step optimal scheduling model was developed to maximize irrigation volume. By introducing a linearly decreasing inertia weight, symmetric velocity bounds, and a stagnation-detection mechanism with Gaussian mutation, DHPSO dynamically balances global exploration and local exploitation, thereby alleviating the premature convergence tendency of standard particle swarm optimization. Benchmark experiments showed that DHPSO achieved competitive optimization accuracy and more reliable convergence behavior on several representative functions compared with baseline algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Hybrid PSO (HPSO). The proposed model incorporates nonlinear constraints associated with photovoltaic output fluctuations, battery state-of-charge thresholds, and pump parameters, and uses a penalty function to handle boundary constraints. Simulations were conducted under three representative meteorological conditions. The results showed that the DHPSO-based variable-speed scheduling strategy increases average irrigation water volume by 3.77% compared with traditional fixed-speed strategies. The proposed method was evaluated in rice irrigation scenarios on the Hangjiahu Plain in northern Zhejiang, demonstrating its potential for intelligent operation of photovoltaic-storage irrigation pump trucks.