The current energy-saving lighting control algorithm still falls into the problem of local optimum. In order to seek for the global optimal solution and improve the energy-saving effect of indoor lighting, a genetic simulated annealing algorithm was designed to optimize the control parameters of lighting system. The local search ability of the algorithm was enhanced by simulated annealing treatment of excellent individuals after genetic manipulation. According to the number of iterations and the fitness of the population, the genetic probability was adaptively adjusted so that the algorithm could enrich the population diversity in the early stage and avoid the prematurity of the algorithm. An illumination model based on artificial neural network was proposed to calculate the indoor illumination distribution and evaluate the illumination comfort, which provided a basis for constructing the fitness function of optimization algorithm. Through simulation experiments, the genetic simulated annealing algorithm was applied in the introduced lighting scenes, and compared with the traditional particle swarm algorithm and genetic algorithm, the lighting energy-saving performance was 5.30% and 13.61% higher respectively.