Diesel engines are widely used in agricultural tractors. During field operations, the tractors operate at low speed and high load for a long time, the fuel efficiency is only about 15% to 35%, and the exhaust waste heat accounts for 38% to 45% of the energy released from the fuel. The use of tractor exhaust waste heat can effectively reduce fuel consumption and pollutant emissions, of which the organic Rankine cycle (ORC)-based waste heat recovery conversion efficiency is the highest. First, the diesel engine map is achieved through the test rig, a plate-fin evaporator is trial-produced based on the tractor size, and the thermodynamic and economic performance model of the ORC are established. Then, taking the thermal efficiency of ORC and the specific investment cost (SIC) as the objective function, the particle swarm optimization (PSO) algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) decision method were used to obtain the optimal operating parameter set under all working conditions. Finally, the results showed that the ORC thermal efficiency could reach a maximum of 12.76% and the corresponding SIC value was 8539.66 $/kW; the ORC net output power could be up to 8.31 kW compared with the system without ORC; and the maximum brake specific fuel consumption (BSFC) could be reduced by 8.3%. The improvement in the thermodynamic performance will lead to a sacrifice in economic performance, and at high speeds, the economic benefits and thermal efficiency reach a balance and show a better thermal economic performance. Recovering exhaust heat energy through ORC can reduce tractor fuel consumption and pollution emissions, which is one of the effective technical means to achieve “carbon neutrality” in agricultural production. At the same time, through the PSO algorithm, the optimal combination of ORC operating parameters is obtained, which ensures that the exhaust heat energy can be effectively recovered during the tractor field operation, and provides a basis for the adjustment of real-time work strategies for future research.