In this work the performance of an industrial hydrogen plant is improved by multi-objective optimization using an adaptation of the nondominated sorting genetic algorithm (NSGA). The heat flux profile on the steam reformer tubes is treated as a decision variable, yielding optimal heat flux profiles for each Pareto solution. The optimization problem has been considered both as a two and three-objective problem. For a fixed feed rate of methane to the unit, simultaneous maximization of product hydrogen and export steam flow rates are considered as the two objectives. The results are better than those obtained in an earlier work by Rajesh et al. (2001), with flue gas temperature in place of the heat flux profile as a decision variable. Minimization of reformer duty was chosen to be the third objective. More useful information is available for the optimal operation of hydrogen plants from three-objective optimization, even though computational time for two and three objectives is comparable.