Studies of individuals sampled in unbalanced clusters have become common in health services and epidemiological research, but available tools for power/sample size estimation and optimal design are currently limited. This paper presents and illustrates power estimation formulas for t-test comparisons of effect of an exposure at the cluster level on continuous outcomes in unbalanced studies with unequal numbers of clusters and/or unequal numbers of subjects per cluster in each exposure arm. Iterative application of these power formulas obtains minimal sample size needed and/or minimal detectable difference. SAS subroutines to implement these algorithms are given in the Appendices. When feasible, power is optimized by having the same number of clusters in each arm k(A) = k(B) and (irrespective of numbers of clusters in each arm) the same total number of subjects in each arm n(A)k(A) = n(B)k(B). Cost beneficial upper limits for numbers of subjects per cluster may be approximately (5/rho) - 5 or less where rho is the intraclass correlation. The methods presented here for simple cluster designs may be extended to some settings involving complex hierarchical weighted cluster samples.