Water quality and biological monitoring provide an indication of the degree to which the natural environment has been affected by anthropogenic‐activities. Currently, this monitoring is both expensive and time‐consuming. A new cost‐effective approach to the design of aquatic monitoring networks is presented. Classical analysis of variance (ANOVA) techniques are reviewed and a modified ANOVA model with control station pairs is suggested. An interactive optimization procedure is presented that incorporates a modified gradient search algorithm to select designs which maximize the statistical power of a network for a specified budget or minimize the cost of a network for a specified statistical power requirement. The sensitivity of the model results are explofed as a function of the cost, the number of sampling stations, replicates, and occasions, the Type I and Type II error, estimates of data variance, and cost components for data describing an aquatic species from a New England power facility. It is shown that for specified power and cost, numerous solutions exist providing the designer with a wide selection of alternatives from which to choose.