Nowadays, fuels with low environmental impact are highlighted in media and society. In this context, hydrogen, as a clean energy source, has a great potential. However, there is still no appropriate infrastructure for its commercialization. The prediction of demand for hydrogen is difficult, generating a high degree of uncertainty in the definition of capacity needs in the future for its logistics network. This work proposes a methodology for the design of the hydrogen supply chain for use in transportation. To represent the problem and evaluate alternatives to invest in logistics infrastructure, a two-stage stochastic mixed-integer programming was developed. The high degree of uncertainty in this chain increases the complexity of the mathematical model, requiring a huge number of scenarios which makes its optimization impossible. To overcome this difficulty, the technique of sample average approximation (SAA) is used. This approach generates solutions, whose quality can be statistically evaluated using a reduced number of scenarios. The proposed methodology was tested in a study case with real data from Great Britain s liquid hydrogen supply chain. The optimal gaps generated in these tests were below 1 percent, demonstrating the adequacy of the developed methodology. Even with the high level of uncertainty of the problem, the propose methodology using SAA technique can define how, when, and where to invest. The results should be helpful in advancing the creation of an appropriate infrastructure for hydrogen commercialization.