Multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-of-the-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest Linux SPL from 5 to 30[Formula: see text]min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.