As an important research issue in software product line, feature selection is extensively studied. Besides the basic functional requirements (FRs), the non-functional requirements (NFRs) are also critical during feature selection. Some NFRs have numerical constraints, while some have not. Without clear criteria, the latter are always expected to be the best possible. However, most existing selection methods ignore the combination of constrained and unconstrained NFRs and FRs. Meanwhile, the complex constraints and dependencies among features are perpetual challenges for feature selection. To this end, this paper proposes a multi-objective optimization algorithm IVEA to optimize the selection of features with NFRs and FRs by considering the relations among these features. Particularly, we first propose a two-dimensional fitness function. One dimension is to optimize the NFRs without quantitative constraints. The other one is to assure the selected features satisfy the FRs, and conform to the relations among features. Second, we propose a violation-dominance principle, which guides the optimization under FRs and the relations among features. We conducted comprehensive experiments on two feature models with different sizes to evaluate IVEA with state-of-the-art multi-objective optimization algorithms, including IBEAHD, IBEA _ε+ , NSGA-II and SPEA2. The results showed that the IVEA significantly outperforms the above baselines in the NFRs optimization. Meanwhile, our algorithm needs less time to generate a solution that meets the FRs and the constraints on NFRs and fully conforms to the feature model.