A new memory gradient method for unconstrained optimization problems is presented. This method makes use of the current and previous iterative information to generate a decent direction and uses exact linear search or Wolfe inexact linear search to define the stepsize at each iteration. The global convergence and linear convergence rate are proved under some mild conditions. Numerical experiments show that the new method is efficient in practical computation.