This paper presents a time two-grid difference (TTGD) method for solving the nonlinear generalized viscous Burgers' equation, which is the first application of the two-grid method to this problem. Based on the Crank-Nicolson finite difference scheme, the TTGD method consists of three computational steps to reduce computational cost compared to the general finite difference (GFD) scheme. The cut-off function method is used to prove the conservation, uniqueness, prior estimate, and convergence of the TTGD scheme in both $ L^2 $-norm and $ L^\infty $-norm on coarse and fine grids. The TTGD scheme is compared with the GFD scheme in Zhang et al. (2021), and the uniqueness of the nonlinear scheme, convergence in $ L^2 $-norm, and prior estimates on both coarse and fine grids are proved. Numerical results show that the TTGD scheme is more efficient than the GFD scheme in terms of CPU time. The method not only improves computational efficiency but also preserves the energy conservation of the original model. The main contributions of this paper are the construction of the TTGD scheme for the nonlinear generalized viscous Burgers' equation, which linearizes the nonlinear term $ u^p u_x $ on the fine grid to improve computational efficiency. In theory, the conservation, existence, uniqueness, prior estimates, and convergence of the TTGD scheme in $ L^2 $-norm and $ L^\infty $-norm on both coarse and fine grids are proved. The paper is organized as follows: Section 2 introduces useful lemmas and notations, and Section 3 establishes the TTGD scheme for the generalized viscous Burgers' equation.This paper presents a time two-grid difference (TTGD) method for solving the nonlinear generalized viscous Burgers' equation, which is the first application of the two-grid method to this problem. Based on the Crank-Nicolson finite difference scheme, the TTGD method consists of three computational steps to reduce computational cost compared to the general finite difference (GFD) scheme. The cut-off function method is used to prove the conservation, uniqueness, prior estimate, and convergence of the TTGD scheme in both $ L^2 $-norm and $ L^\infty $-norm on coarse and fine grids. The TTGD scheme is compared with the GFD scheme in Zhang et al. (2021), and the uniqueness of the nonlinear scheme, convergence in $ L^2 $-norm, and prior estimates on both coarse and fine grids are proved. Numerical results show that the TTGD scheme is more efficient than the GFD scheme in terms of CPU time. The method not only improves computational efficiency but also preserves the energy conservation of the original model. The main contributions of this paper are the construction of the TTGD scheme for the nonlinear generalized viscous Burgers' equation, which linearizes the nonlinear term $ u^p u_x $ on the fine grid to improve computational efficiency. In theory, the conservation, existence, uniqueness, prior estimates, and convergence of the TTGD scheme in $ L^2 $-norm and $ L^\infty $-norm on both coarse and fine grids are proved. The paper is organized as follows: Section 2 introduces useful lemmas and notations, and Section 3 establishes the TTGD scheme for the generalized viscous Burgers' equation.