In this paper, we surveyed the implementations of genetic algorithm within networks, whether it is computer network, transportation network, and other fields that have networking context. Their feasibilities are discussed along with our suggestions for potential improvements. Genetic algorithm can also be an alternative to other optimization methods/algorithms. In some cases, it even outperforms other methods. However, the choice of genetic algorithm might be influenced by some concerns, such as execution time and problem size. Generally, genetic algorithm process will accomplish according to its parameters sizes. Finally, the success stories prove the applicability, adaptability, and scalability of genetic algorithm, specifically for almost-any network optimization. © 2014 IEEE.