Soil pollution control is a key area in environmental protection, and with the acceleration of urbanization, the demand for remediation of polluted land is increasing. This paper investigates multi-objective optimization algorithms in soil pollution control and proposes a hybrid multi-objective genetic algorithm combined with local search for remediation path design. The method includes a global search using NSGAII algorithm combined with HCS local search strategy to improve the quality of search efficiency and remediation. The study constructed a multi-objective optimization function for economic and environmental benefits based on different land types and restoration technologies, and optimized the decision-making by simulating the restoration costs and land benefits under different scenarios. The results show that during the restoration process, the unit land benefit increases and the restoration cost decreases gradually with the increase of volume ratio. The unit restoration cost when maximizing the land benefit is 0.05 million yuan/m², while the unit land benefit can reach 18,200 yuan/m² when minimizing the restoration cost. The optimized scheme significantly improved the economic benefits and effectively reduced the environmental impacts, providing a scientific basis for the remediation and redevelopment of contaminated soil.
