Aimed at the problem that the Grey Wolf Optimization (GWO) algorithm is easily bogged down in local optimization in solving function optimization, this paper proposes a nonlinear adjustment strategy by adopting sinusoid, logarithmic, tangential, cosine and quadratic curves. And at the same time a strategy on mutating position of the agents is presented, whose position is influenced by fitness value. The experimental results for three standard test functions show that the proposed cosine and the quadratic curve strategies are superior to the classical linear strategy, and the others such as the sinusoid strategy, the logarithmic strategy, and the tangential curve strategy are inferior to the linear strategy.