Wastewater in the chemical industry can be minimized by water reuse. The optimum water usage network leads to a minimum freshwater consumption and wastewater treatment. Genetic algorithm, an objective of this work, is developed for solving the wastewater minimization problem. The optimization model is formulated for both single and multiple contaminants in the class of mixed integer nonlinear programming (MINLP). In this model, there is a set of equality constraints from mass balance that drives the genetic algorithm from addressing the feasible solution. To elucidate this problem, all variables have to be divided into two groups: independent and dependent variables. The values of independent variables come from randomization, whereas the values of dependent variables come from simultaneous solutions of a set of equality constraints after assigning the values of independent variables. This method is applied to the steps of initialization, crossover and mutation. The developed program is the MATLAB(TM) toolbox that consists of 29 m-file. The graphic user interface was created in order to make the program easy and convenient. After a user inputs the process condition of the problem into the blank form of GUI, the program will formulate the optimization model and solve for the solution automatically. Then, the optimum results are returned to the user. The program is tested with a process that contains a certain number of water-using operations in which water is used to remove a fixed content of contaminant. In the single contaminant system, this program and Lingo can find the minimum freshwater consumption, but the solutions are different in the configuration of water usage network. These alternative configurations give a wide-vision on the analysis of the system. In the multiple contaminants system, this program can find the same or better results in some test problems. (C) 2003 Elsevier B.V. All rights reserved.