Biomass is a renewable resource that has attractive characteristics for energy production, but the corresponding supply chain could be subject of several uncertain factors that can affect drastically the optimal configuration, and those have not been properly accounted in previous publications. Therefore, this work presents a new approach for the optimal planning under uncertainty for a biomass conversion system involving simultaneously economic and environmental issues. The environmental impact was measured via the Eco-indicator99 method and the economic aspect was determined through the net annual profit. The proposed method considered the uncertainty involved in the raw material price by the stochastic generation of scenarios using the Latin Hypercube method followed by the implementation of the Monte-Carlo method, where a deterministic optimization problem was solved for each single scenario to select the structure of the more robust supply chain relying on statistical data. The proposed approach was applied to a case study for a distributed biorefinery system in Mexico. The results showed that the behavior of the profit values for the stochastic case is not associated to the behavior of the raw material price; also, it is possible to observe that the supply chain topology could be affected for the uncertainty in the raw material price; however, the environmental and economic objectives did not present significant changes.