The use of fuels with low environmental impact has been recently highlighted in the media. In this context, the use of hydrogen as a fuel has been considered an alternative with significant potential to integrate a more sustainable energy matrix. However, there is still no appropriate infrastructure available for its commercialization. This study proposes a methodology for designing a hydrogen supply chain while considering the inherent uncertainty associated with the demand for this fuel in the future. To represent the problem and evaluate investment alternatives for the logistic infrastructure, an optimization model is proposed based on two-stage stochastic mixed-integer programming. To obtain solutions from the proposed model, the sample average approximation (SAA) method is used to obtain statistically certified solutions from a reduced number of scenarios. The proposed methodology was applied to the design of Great Britain’s liquid hydrogen supply chain using real data. The proposed framework was able to provide solutions with optimality gaps estimated to be below 1% within an acceptable computational time, demonstrating the adequacy of the developed methodology.