The data processing in multispectral thermometry remains a huge challenge due to the unknown emissivity. In this article, a novel data processing model of multispectral thermometer is established by adding new constraints of emissivity on the basis of object function. The new two algorithms for model optimizing, Sequential Randomized Coordinate Shrinking (SRCS) and Multiple-Population Genetic (MPG), are introduced. The temperature and emissivity of two samples are calculated by MPG algorithm to prove the validity of the MPG algorithm in practical application. The experiments reveal that the relative error of temperature is within 0.4% with the average calculation time of 0.36 s. The method proposed in this article can realize the simultaneous estimation of temperature and emissivity without emissivity assumption model, which is expected to be applied to real-time measurement of temperature in industrial fields.