Abstract: The problem of a temperature-dependent-parameters identification has been researched in a steady-state temperature environment, and two methods, the whole domain method of a multi-objective and the equivalent piecewise method based on metamodeling, are developed for parameters-identification problems. The former method is designed to construct different temperature-distribution forms for an optimization objective. Then, the improved method of non dominated sorting genetic algorithm (NSGA-II), as a multi-objective optimization method, is employed to estimate the thermophysical parameters based on metamodels. This methodology could improve the search efficiency and circumvent difficulties of an ill-posed problem. In the latter method, the metamodeling of residuals between calculated and experimental results was constructed to identify equivalent parameters of each temperature section, and then regression analysis was used to identify the law of parameters varying with temperature. Finally, examples were given to demonstrate the effectiveness of these two methods. The results show that the whole domain method performs better in robusticity, while the equivalent piecewise method has a better maneuverability.