The control and optimization of combustion status during corpse incineration is of great significance for energy conservation and emission reduction. In recent years, there has been little research on the control and optimization of corpse combustion status. We first preprocessed the data; for the corpse incineration data, we aligned data from multiple sources based on time as a reference. Then, using a heuristic approach, we performed corpse segmentation. Finally, we employed a method based on genetic algorithms, adapting the genetic algorithm for corpse incineration optimization to achieve control of flue gas and energy consumption during corpse incineration. Specifically, for this control optimization algorithm, after calculating the fitness, we input the new controllable parameters of the incinerator into our pollution level and energy consumption classification model. Through the output state of this classification model, we iteratively adjusted the input parameters at the current moment until a low pollution and low energy consumption state is achieved. Simple comparative experiments indicate that within a limited number of iterations, our method can continuously optimize parameter combinations compared to random selection, resulting in individuals with higher fitness.