Combination of back propagation (BP) neural networks and genetic algorithm (GA) is used to optimize process parameters of the thick nanostructured Al2O3-13%TiO2 (mass fraction) ceramic coating prepared by laser multi-layer cladding technique. The neural model is trained based on the experimental results of the orthogonal test including three factors and three levels. It is developed to express the relationship between coating properties (bonding strength and microhardness) and process parameters (closed-loop controlling temperature of molten pool, ultrasonic vibration frequency and preheating temperature of incubator). Meanwhile, the bonding strength and microhardness of the nanostructured ceramic coating are optimized by single-objective and multi-objective optimization methods based on the genetic algorithms. The results show that the prediction data of genetic neural networks model agree well with the experimental values, and the relative error is less than 2.5%. The maximum bonding strength and microhardness of the coating are 70.7 MPa and 2025.5 HV, respectively. The process parameters of closed-loop controlling temperature in molten pool, ultrasonic vibration frequency and preheating temperature in incubator are set to 2472.0 ℃, 31.9 kHz and 400 ℃ when the bonding strength and microhardness have the same weight. At this state, the overall performance of the coating is the best and the bonding strength and microhardness of the coating are 69.1 MPa and 1835.5 HV, respectively.