Genetic algorithm based optimizers have to balance extensive exploration of solution spaces to find good solutions with convergence to generate solutions quickly. Many optimizers use a two phase approach where the first phase explores the solution space and the second converges on a set of potential regions. This paper describes a meta-level algorithm (GA_ITER) that iteratively applies a GA based optimizer with a bias towards either exploration or convergence. The optimizer is executed with a very small number of evaluations which leads to fast generation of solutions. The iterative approach of GA_ITER has been shown to lead to fast generation of good solutions. Experiments in problems from two real-world domains have shown that GA_ITER can improve the performance of an existing GA without compromising the quality of the solution.