In recent years, researchers have focused on improving the design of building envelopes to enhance their environmental performance using kinetic systems, such as kinetic shading screens. Research has shown that these systems can effectively control and improve daylight illuminance in a room (Fiorito et al. in Renewable and Sustainable Energy Reviews 55:863–884, 2016). However, finding their best configuration for given conditions is challenging because it depends on a variety of factors such as room size, orientation, and use, as well as the design parameters of the screen itself. This chapter describes research that compares two different approaches to the problem considering daylight performance and design variety. Focusing on a case study, it uses a simulation model to calculate the performance of configurations on four days of the year—equinoxes and solstices. The first approach is to create a catalog through brute-force enumeration from a limited space of possible design configurations and then select the best for every hour of the day. The second approach is to consider a larger design space, but sample possibilities using a smaller set of master variables that algorithmically control the states of multiple flaps. The performances of configurations identified by both approaches are compared, and then the benefits and challenges of each are discussed. The study concludes that the second approach (algorithmic sampling) can search a wider and more diverse space of solutions and find configurations with better performance. In addition, although it takes more time, it is more efficient, considering the size space being browsed.