Teachers often group students into teams to organize their classrooms and network-informed interventions hold great promise as a way to facilitate positive peer influence and promote the diffusion of intervention effects. Yet thus far, relatively little research has explored how teachers or prevention scientists can best use social network information to assign students to teams. The goal of the present study was to identify and compare seven methods that use different data sources and assignment algorithms to create teams of students. To test these methods, we used survey data from 247 5th through 8th grade students in three rural schools that assessed students' social networks, sociability, values and interests, and bonding to school. To create teams, we first identified popular students (i.e., those who received the highest number of peer nominations) who also had school bonding scores in the normal range and formed 4-person teams around them, applying different methods to assign students to teams. In all but one method, we placed at-risk students (i.e., those who had the lowest school bonding scores) in teams only during the final round of team creation. Team assignments were compared against three criteria: (1) team-level bonding to school, (2) patterns of affiliation among teammates, and (3) shared values and interests. Two methods, one that used only social network data and one that used social network data in combination with students' values and interests, yielded the most promising outcomes. The most positive results were obtained when a pruning algorithm akin to the one proposed by Girvan and Newman (2002) Proceedings of the National Academy of Sciences, 99, 7821-7826 was used to select which dyads to join as teammates; this pruning method joined more weakly linked students first, maximizing their potential to find suitable matches. These methods for team assignment hold promise for designing network-informed school-based interventions.