In the research on energy optimization control for parallel hybrid tractors, torque has been identified as a crucial factor influencing the tractor's fuel economy, operational efficiency, and agricultural development. This study focuses on the tractor's overall demand torque as the primary research parameter and designs a comprehensive scheme for the parallel hybrid tractor. The study includes the design of power system parameters and the construction of a dynamic model for the entire machine. Based on this, an energy optimization control strategy, termed adaptive immune particle swarm optimization fuzzy control strategy (AIPSOFCS), is proposed. Simulation analysis is performed using representative plowing conditions, and AIPSOFCS is compared with the power follow control strategy (PFCS) and fuzzy control strategy (FCS). The results indicate that AIPSOFCS demonstrates higher fuel economy and operational efficiency compared to PFCS and FCS. In the plowing conditions, the fuel economy of AIPSOFCS is reduced by 8.45% and 2.93% compared to PFCS and FCS, respectively. In the rotary tillage conditions, the fuel economy of AIPSOFCS is reduced by 2.40% and 4.07% compared to PFCS and FCS, respectively. Finally, hardware-in-the-loop (HIL) testing of the controller confirms the effectiveness of AIPSOFCS. This research is of significant importance for enhancing the fuel economy and operational efficiency of parallel hybrid tractors and provides theoretical support and reference for the future.