A novel strategy using the Sobol'-Simulated Annealing algorithm was proposed to reduce the number of optimization steps and guarantee the accuracy of a molten salt solar power tower plant design. The new method combined the Sobol' method and the Simulated Annealing algorithm for global sensitivity analysis and global optimization, respectively. Based on the sensitivity analysis, the high-dimension global optimization problem was transformed into several low-dimension global optimization problems by parameter decoupling. In order to obtain the global minimum levelized cost of electricity of the solar power tower plant, these low-dimension models were successively optimized by utilizing the Simulated Annealing algorithm. A reference case study of the solar power tower plant with 2650 heliostats was conducted. The heliostat field, receiver, thermal storage system and power block were designed as a function of 12 parameters. It was demonstrated that the parameters related to the heliostat field and receiver were almost independent. The minimum levelized cost of electricity of 22.22 (sic)/kWh(e) was obtained. Furthermore, a comparison with the global algorithm and local algorithm showed that the novel method could reduce the number of optimization steps by approximately 75% compared with that of the global algorithm. A much more accurate optimal design than that of the local algorithm can be achieved herewith.