With the increase of energy demand and environmental protection requirements in rural areas, the energy efficiency and sustainability of traditional electric heating systems are facing severe challenges. In order to optimize the coupling effect of rural distributed electric heating systems and renewable energy, this paper proposes an optimization method based on genetic algorithm (GA). First, this paper constructs a mathematical model of the coupling of distributed electric heating and renewable energy, and designs a multi-objective optimization problem considering the volatility of renewable energy (such as wind energy and solar energy) and the time-varying characteristics of electric heating demand. Then, GA is used to optimize the key parameters in the system, including energy scheduling, equipment configuration and load forecasting. In order to improve the optimization effect of GA, this paper improves the traditional genetic algorithm, including the introduction of adaptive crossover, mutation probability and the combination with local search strategy. In the experimental conclusion, the energy consumption of the optimized system was reduced by about 16% compared with the traditional system, the energy utilization rate was improved by 3.4%, and the load balance error of the system was reduced from 12.85 to 9.46 kW. More importantly, the carbon emissions of the optimized system are reduced from 1185.4 kg CO₂ to 63.4 kg CO₂. In the above data conclusions, the optimization method based on genetic algorithm can effectively improve the coupling efficiency of rural electric heating system and renewable energy, reduce the environmental burden, and promote the realization of low-carbon economy.