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Desalination is known to have considerable energy, economic, and environmental impacts. Treatment trains are receiving increased interest for their potential to meet produced water standards while both minimizing impacts and increasing the range of eligible input salinities. However, determining which technologies to combine and predicting their performance is both difficult and case specific. This research will present a unique hybrid-modelling framework (DESALT) for evaluating and comparing desalination treatment trains based on the same customizable inputs. This comprehensive discrete-based approach generates treatment trains and then systematically evaluates them using physics-based evaluation methods that are reflective of changes in operating conditions. DESALT also accounts for technology limitations, product water requirements, and user preferences. The modelling outputs are filtered using a combination of a Pareto front analyses and DEA decision support. The result is a list of eligible and preferred treatment trains with their corresponding operating conditions. The framework's performance was tested by applying two different technologies (electrodialysis and brackish water reverse osmosis) to a brackish water case study. While the methodology was able to capture the trade-offs between treatment trains and individual technologies, the results are highly reliant on the accuracy of the evaluation methods used.