The combined use of multiobjective optimization and life-cycle assessment (LCA) has recently emerged as a useful tool for minimizing the environmental impact of industrial processes. The main limitation of this approach is that it requires large amounts of data that are typically affected by several uncertainty sources. We propose herein a systematic framework to handle these uncertainties that takes advantage of recent advances made in modeling of uncertain LCA data and in optimization under uncertainty. Our strategy is based on a stochastic, multiobjective, and multiscenario mixed-integer nonlinear programming approach in which the uncertain parameters are described via scenarios. We investigate the use of two stochastic metrics: (1) the environmental impact in the worst case and (2) the environmental downside risk. We demonstrate the capabilities of our approach through its application to a generic complex industrial network in which we consider the uncertainty of some key life-cycle inventory parameters. (c) 2014 American Institute of Chemical Engineers AIChE J, 60: 2098-2121, 2014