In many cases, Software Product Line Testing (SPLT) targets only the selection of test cases which cover product features or feature interactions. However, higher testing efficiency can be achieved through the selection of test cases with improved fault-revealing capabilities. By associating each test case a priority-value representing (or aggregating) different criteria, such as importance (in terms of fault discovered in previous test campaigns), duration or cost, it becomes possible to select a feature-covering test suite with improved capabilities. A crucial objective in SPLT then becomes to identify a test suite that optimizes reaching a specific goal (lower test duration or cost), while preserving full feature coverage. In this article, we revisit this problem with a new approach based on constraint optimization with the NValue and GlobalCardinality constraints and a sophisticated search heuristic. These constraints enforce the coverage of all features through the computation of max flows in a network flow representing the coverage relation. The computed max flows represent possible solutions which are further processed in order to determine the solution that optimizes the given objective function, e.g., the smallest test execution costs. Our approach is implemented in a tool called Flower/C and experimentally evaluated on both randomly generated instances and standard benchmarks. Comparing Flower/C with MiniSAT+ and Cplex, stateof-the-art tools for constraint optimization, we show that our approach is competitive with both tools on random instances and benchmarks. Our results show that MiniSAT+ is not competitive at all, whereas when the priority-value of each test case is uniformly set to 1, that Flower/C approaches Cplex in performance. We compare four different models of Flower/C, using different global constraints, and the one mixing different constraints shows the best performance with high reduction rates. These results open the door to an industrial adoption of the proposed technology.