The work aims to explore the influence of the content of each coatings component on the coatings water contact angle (WCA) and thermal conductivity, and find the optimal composition so as to maximize the WCA thermal conductivity simultaneously. The coatings samples were made and tested according to the L25(55) orthogonal experimental design. The BP neural network with the structure of 5-8-2 was established by Matlab software. The prediction model of WCA and thermal conductivity of coatings was obtained by training and testing the neural network with the results of orthogonal test. The genetic algorithm was used to optimize the content of each component by calling the trained prediction model. The optimized and adjusted parameters were used to test and verify the optimization results. After the BP neural network model was trained, the prediction results showed that the maximum error was 0.061 98 and WCA 0.065 77 for thermal conductivity. Based on the optimization results of genetic algorithm, the coatings would have 164.24° for WCA and 14.19 W/(m.K) for thermal conductivity, with 10.1wt% Nano-SiO2, 6.4wt% TiO2, 5wt% carbon powder, 0.6wt% nano graphene and 1.8wt% MTES. In the meanwhile, the error of the WCA and thermal conductivity was 3.80% and 2.31%, respectively. The coatings made with adjusted parameters had 155.02° for WCA and 13.25 W/(m.K) for thermal conductivity, with errors of 5.64% and 5.58%, respectively. Through BP neural network prediction model and genetic algorithm optimization, the water contact angle and thermal conductivity of coatings both got greatly improved.