In this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search, are adopted for increasing accuracy and expanding searching space simultaneously. A Gauss distribution function is employed as a technique to enhance the accuracy of this algorithm. POA is evaluated using 23 well-known test functions, 38 IEEE CEC benchmark test functions (CEC 2017, CEC 2019) and three real engineering problems. The statistical results of the benchmark functions show that POA can provide very competitive and promising results. Not only does POA require a relatively short computational time for solving problems, but also it shows superior accuracy in terms of exploiting the optimum.