This paper introduces a time two-grid finite difference (TTGD) scheme for solving the nonlinear generalized viscous Burgers' equation. The TTGD scheme is designed to reduce computational costs compared to the general finite difference (GFD) scheme by linearizing the nonlinear term \( u^p u_x \) on the fine grid. The method is based on the Crank–Nicolson finite difference scheme and consists of three computational procedures. The paper proves the conservation, unique solvability, prior estimates, and convergence in \( L^2 \)-norm and \( L^\infty \)-norm for the TTGD scheme on both coarse and fine grids. The numerical results show that the TTGD scheme is more efficient in terms of CPU time compared to the GFD scheme, and it also preserves the energy conservation of the original model. The theoretical analysis provides a direct proof of the uniqueness of the solution and convergence in \( L^2 \)-norm, enhancing the efficiency and accuracy of the method.This paper introduces a time two-grid finite difference (TTGD) scheme for solving the nonlinear generalized viscous Burgers' equation. The TTGD scheme is designed to reduce computational costs compared to the general finite difference (GFD) scheme by linearizing the nonlinear term \( u^p u_x \) on the fine grid. The method is based on the Crank–Nicolson finite difference scheme and consists of three computational procedures. The paper proves the conservation, unique solvability, prior estimates, and convergence in \( L^2 \)-norm and \( L^\infty \)-norm for the TTGD scheme on both coarse and fine grids. The numerical results show that the TTGD scheme is more efficient in terms of CPU time compared to the GFD scheme, and it also preserves the energy conservation of the original model. The theoretical analysis provides a direct proof of the uniqueness of the solution and convergence in \( L^2 \)-norm, enhancing the efficiency and accuracy of the method.