In the past decade genetic algorithms (GAs) have been used in a wide array of applications within the water resources field. Although usage of GAs has become widespread, the theoretical work from the genetic and evolutionary computation (GEC) field has been largely ignored. Most practitioners have instead treated the GA as a black box, specifying the parameters that control how the algorithms navigate the spaces of each application using trial-and-error analysis. Trial-and-error analysis is a time-consuming, difficult process resulting in an arbitrary selection of parameters without any regard to the fundamental properties of the GA. The concept of "competent search and optimization" as discussed in this work addresses this difficulty by using the available theoretical work from the GEC field to set the population size, the selection pressure, account for potential disruptions from crossover and mutation, and prevent drift stall. This paper provides an overview of a three-step method for utilizing GEC theory to ensure competent search and avoid common pitfalls in GA applications. Copyright ASCE 2004.