The optimization problem about the sum of two convex functions has been received much attention in recent years, in which one of them is differentiable with Lipschitz continuous gradient, and the other one contains a bounded linear operator. In this paper, an over-relaxed primal-dual fixed point algorithm is proposed to solve such problem. Compared with the original primal-dual fixed point algorithm, the proposed algorithm expands the selection range of relaxation parameters. By defining a suitable norm and using the fixed point theory of nonexpansive operators, we prove the convergence of the proposed iterative algorithm and together with the ergodic convergence rate. Under some strong conditions of the objective function, we prove that the algorithm has a global linear convergence rate. Finally, we apply the proposed algorithm to solve the total variation image restoration model to verify the validity of the proposed algorithm. Numerical results show that the primal-dual fixed point algorithm with the relaxation parameter larger than one (i.e., over-relaxation) converges faster than the relaxation parameter less than one.