A new hybrid evolutionary algorithm is proposed to solve the parallel machine scheduling problems with step-piece deteriorating processing time. The goal is minimizing the total completion time. The algorithm uses opposing strategy and Smallest Rate First (SRF) rule to generate the initial population to improve its quality, and the algorithm considers the population diversity to accelerate the convergence of the algorithm, which improves the calculation efficiency of the algorithm. At the same time, a variable neighborhood search algorithm with 3-opt perturbation operator is added to improve the quality of the results obtained by the genetic algorithm. By simulating the experiments of different scale examples, the results are improved compared with the traditional GA and VNS algorithms.