In a large scale sampling survey situation, a two- or multi-stage cluster sampling design is often used to save the sampling effort. Consequently, estimation precision would be sacrificed, and often it would be difficult to estimate the subpopulation of interest since secondary sampling units are independently selected within each selected primary sampling units, hence, the within-subpopulation sample size often cannot be controlled. In order to make balance between the sampling cost and the estimation precision, a modified two-stage cluster sampling design is constructed and investigated in this research. A set of primary units is selected in the first stage by some probability design, and then the sampling population of the second-stage sampling is composed of the integration of all the secondary units within the selected primary units. Therefore, the second-stage sample can be selected with more flexibility. Various combinations of the first- and second-stage designs are studied together with different estimators to investigate the property of this sampling design. The performance are also compared with other comparable conventional designs.