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• New membrane cascade configurations improved the fractionation while keeping the stage number at 3. • Selecting the best set up with many performance indicators was performed using a multicriteria decision making approach. • Backward analysis was able to pinpoint critical parameters. • This design procedure is applicable for other integrated processes with multiple performance indicators. Membrane cascades can fractionate fructooligosaccharides into 3 different fractions with varying degrees of polymerization (DP). In contrast to the traditional membrane system, membrane cascades have flexibility in configuration and setup for each stage. Apart from the improvement flexibility of the cascades provides, it raises problems related to multiple performance indicators and multiple process parameters. Therefore, new design criteria are required. We have designed an optimization approach for this multi-criteria problem. Eight configurations of cascaded membranes were built, measured and simulated to develop a design strategy. The performance of the separation process was evaluated by 10 different indicators: purities and yields for 3 different fractions and 4 separation factors between molecules with an adjacent DP. We found that the proposed configurations exceeded the performance of the previously reported 3-stage membrane cascade. Within those configurations, the cascade designs were able to increase the purity of (1) monosaccharides to 47% from 9%, (2) DP3 to 34% from 24% and (3) DP ≥ 5 to 77% from 34%. We also report a procedure to select a single optimum combination that compromises all performance indicators. This procedure systematically calculated the weights, which were then used to rank all feasible combinations and select the best one. In addition, a backward analysis using sensitivity coefficients was performed to pinpoint critical process parameters. Knowing these parameters, more targeted and more efficient improvements could be made. This approach is applicable for most integrated systems with multi-process variables and multi-performance indicators combining process modelling and multi-criteria decision making. [ABSTRACT FROM AUTHOR]