Open idea evaluation is based on the vision of leveraging the crowd’s wisdom for screening and evaluation of early stage innovations. However, when being confronted with the task to evaluate a large number of idea proposals, cognitive capacity of participants in open idea evaluation is challenged. Given the diminishing effect of cognitive load on the ability to make elaborate deci-sions, this paper aims to answer the question of how to lower cognitive load of participants in open idea evaluation. Therefore, we leverage knowledge from research in choice architecture, a concept incorporating tools to influence decisions by the design of decision situations. We derive two design variations–partitioning of options and decision staging–and propose an experimental design for their evaluation in a laboratory experiment. With the proposed study, we aim to con-tribute to theory by combining knowledge from choice architecture with the design of crowdsourcing platforms. Consequently, we aim to provide novel insights into decision support for crowdsourced decision tasks.