Abstract: Query optimization is essentially a complex search task - a search for the best possible plan from among the semantically equivalent plans that can be generated for any given query. It therefore seems logical to consider query optimization in terms of search algorithms. Various search algorithms have been applied by researchers to find an optimal plan for query execution. With queries getting more and more complex the search complexity is increasing. Exhaustive techniques are adequate for trivial instances only, while combinatorial optimization techniques are vulnerable to the peculiarities of specific instances. Current query optimization techniques are thus, inadequate to support some of the emerging database applications. Genetic Algorithms (GAs) are becoming a widely used and accepted method for very difficult optimization problems. This study reviews the studies carried out on application of Genetic Algorithms to Database Query Optimization. The application of Genetic Algorithm to query optimization is motivated by GA’s robustness and efficiency in a wide area of search problems. From the studies reviewed it turns out that Genetic Algorithms are a viable alternative to existing query optimizers for optimization of very large queries.