Project portfolio frameworks usually present the selection and adjustment phases sequentially, lacking the perspective that these phases can be performed iteratively, allowing feedback looping. This paper aims to narrow this gap by investigating the interactions between selection and adjustment phases, and the effects on project portfolio efficiency. Besides, the effect of the control variables—time, effort, and requirements–is investigated along the project design phase. The research approach was based on simulation with five anecdotal projects and real portfolio data gathered in a case-based approach with ten projects. As a result, an integrated portfolio selection and adjustment framework applying data envelopment analysis (DEA) is presented. The dominant portfolios are determined based on mean-Gini selection to construct an efficient frontier comparing return and risk of each one. The proposed framework enables adjustment by evaluating the impact of control variables via two-stage DEA. The results show that the proposed framework indeed increases the number of suitable portfolios. Furthermore, the framework demonstrates how efficiency is impacted by the control variables of a project.