In this work, we revisit the design and implementation of the Branch-and-Bound (B&B) algorithm for heterogeneous environments combining multi-core processors with GPU accelerators. The challenge is to automatically identify the best mapping of computations to the target heterogeneous platform and to adjust the problem, algorithmic and platform parameters. In the proposed meta-algorithm, four hardware configuration scenarios have been considered: 1CPU-1GPU, nCPU-0GPU, nCPU-1GPU, nCPU-nGPU.Over a serial B&B, accelerations up to ×222 have been achieved for large problem instances of the Flow-Shop scheduling problem. The reported results show that: (1) the GPU-accelerated algorithm performs better than its multi-core CPU-based version; (2) combining multi-core and GPU allows improvement up to 36% over a single CPU-GPU execution; (3) the more GPU devices are used, the better the speedups are whatever is the considered problem instance.