When well-implemented, mediation analyses play a critical role in probing theories of action because their results help lay the ground work for the critical development of a treatment and the iterative advancement of theories that are foundational to a discipline. Despite strong interest in designs that incorporate mediation, few studies have developed effective and efficient strategies to plan experiments examining multilevel mediation. We probe several design strategies for cluster-randomized designs and derive sampling plans that maximize power under cost constraints. The results suggest that among the more durable design strategies for mediation is covariance adjustment on variables predictive of the outcome and optimal sample allocation. The statistical power and optimal sample allocation results are implemented in the R package PowerUpR.