Using the time-temperature superposition principle,the dynamic properties of viscoelastic materials can be shifted to obtain a master curve.A shifting method based on the Generalized Maxwell Model(GMMBS) ,is proposed for the time-temperature superposition process of thermo-rheological simple,linear viscoelastic materials.Experimental data points under different temperatures are all considered as a whole and expressed with one unified representation by the GMMBS,which utilizes the feature that the Generalized Maxwell Model can describe a large class of viscoelastic materials with needed accuracy.Compared with traditional overlapping window based shifting methods,the proposed constitutive model based method needn’t judge the size or existence of the overlapping window first,and computes shift factors with useful information contained in all experimental data points.The effectiveness of the proposed method is verified by simulated data,generated from published test results,with various experimental noise levels,densities of data points and sizes of overlapping windows.It has been shown that the GMMBS is robust and accurate. Using the time-temperature superposition principle, the dynamic properties of viscoelastic materials can be shifted to obtain a master curve. A shifting method based on the Generalized Maxwell Model (GMMBS), is proposed for the time-temperature superposition process of thermo-rheological simple , linear viscoelastic materials. Experimental data points under different temperatures are all considered as a whole and expressed with one unified representation by the GMMBS, which utilizes the feature that the Generalized Maxwell Model can describe a large class of viscoelastic materials with needed accuracy. Compared with traditional overlapping window based shifting methods, the proposed constitutive model based method need not judge the size or existence of the overlapping window first, and computes shift factors with useful information contained in all experimental data points. The effectiveness of the proposed method is verified by simulated data, generated from published test results, with various ex perimental noise levels, densities of data points and sizes of overlapping windows. It has been shown that the GMMBS is robust and accurate.