Abstract: The Noah-MP (Noah land surface model with Multi-Parameterizations) provides numerous complex parameterization schemes for land surface physical processes. However, a Noah-MP LSM (Land Surface Mode) simulation is often limited by a large calculational requirement and an insufficient computational ability. Therefore, determining an applicable parameterization scheme through full combination scheme experiment is difficult. To scientifically reduce the number of experiments, an orthogonal test method was introduced in this study, and five primary factors affecting surface temperature simulations were selected—dynamic vegetation, canopy stomatal resistance, soil moisture factor for stomatal resistance, surface layer drag coefficient, and radiative transfer. To determine an applicable combination of parameterization schemes for studying parametric scheme optimization, nine experiments were designed using the CLDAS-V2.0 (China Meteorological Administration Land Surface Data Assimilation System) to drive the Noah-MP LSM for surface temperature simulation in Southeast China. Results show that the choice of a parameterization scheme had a substantial impact on the simulation of a woodland area as well as the simulation in July and August. The sensitivity of the land surface physical process and the optimal parameterization scheme were simultaneously affected by the underlying surface and season. In most cases, dynamic vegetation and canopy stomatal resistance substantially affected the simulation and were relatively sensitive physical factors. Through a spatiotemporal analysis of the optimal combinations of parameterization schemes for different underlying surfaces in different seasons, a simulation verification showed that the optimal combination for surface temperature simulation in southeast China was the following: Dynamic vegetation, Ball–Berry-type canopy stomatal resistance, Noah-type soil moisture factor, M-O-type surface heat exchange coefficient, and GAPFVEG type radiative transfer schemes.