Large-scale experiments that involve nested structures may assign treatment conditions either to subgroups such as classrooms or to individuals such as students within subgroups. Key aspects of the design of such experiments include knowledge of the variance structure in higher levels and the sample sizes necessary to reach sufficient power to detect the treatment effect. This study provides methods for maximizing power within a fixed budget in three-level block randomized balanced designs with two levels of nesting, where, for example, students are nested within classrooms and classrooms are nested within schools, and schools and classrooms are random effects. The power computations take into account the costs of units of different levels, the variance structure at the second (e.g., classroom) and third (e.g., school) levels, and the sample sizes (e.g., number of Level-1, Level-2, and Level-3 units).