The standard Grey Wolf Optimization (GWO) algorithm has a few disadvantages of low solving precision, slow convergence, and bad local searching ability. In order to overcome these disadvantages of GWO, an Improved GWO (IGWO) algorithm was proposed to solve constrained optimization problems. Using non-stationary multi-stage assignment penalty function method to deal with the constrained conditions, the original constrained optimization problem was converted into an unconstrained optimization problem. The proposed IGWO algorithm was applied to solve the converted problem. In proposed IGWO algorithm, good point set theory was used to initiate population, which strengthened the diversity of global searching. Powell search method was applied to the current optimal individual to improve local search ability and accelerate convergence. Simulation experiments were conducted on the well-known benchmark constrained optimization problems. The simulation results show that the proposed algorithm not only overcomes shortcomings of the original GWO algorithm, but also outperforms differential evolution and particle swarm optimization algorithms.