This paper presents a dynamic perturbation grasshopper optimization algorithm for engineering design problems. Three effective strategies were proposed to avoid the drawbacks of the original GOA that falling into local optimum easily and had a slow convergence speed. First, a dynamic attenuation adjustment factor was introduced to balance the exploration and exploitation in the search space and accelerated the convergence speed. Then, Cauchy inverse cumulative distribution function was employed for modifying the grasshopper’s position to increase the randomness of each grasshopper’s movement and improved the algorithm on the global optimization ability. Finally, Gaussian mutation will take smaller steps allowing for every corner of the search space to be explored in a much better way. To testify the effectiveness of the proposed strategies to the algorithm improvement, the proposed DPGOA was tested by 17 benchmark functions and the statistical tests contain Friedman test and Wilcoxon rank-sum test. As the experimental results showed, the proposed DPGOA was significantly superior to other natural-inspired algorithms both in convergence speed and accuracy. Finally, the competitive results of two real-world engineering problems illustrates the proposed DPGOA can be deployed to constrained optimization problems or other fields for future work.