Compared to the widely used gradient-based optimization techniques, random search methods have the advantages of easy computation, simple implementation and global optimality; especially when constraints are involved, or when the gradient is burdensome or impossible to calculate. In this paper, several heuristic principles are proposed to enhance the performance of the random optimization. Several examples are used to illustrate the advantages of heuristic random optimization over random and gradient-based methods.