Many combinatorial optimization problems are modeled in practice as permutation-based ones. We have recently proposed a new data structure called IVM dedicated to those problems. IVM is memory efficient in terms of size and management time for solving large permutation problems using Branch-and-Bound (B&B) algorithm. We believe that those memory properties make IVM well-suited for Many Integrated Cores (MIC) architecture. This paper deals with the parallel design and implementation of the B&B algorithm on MIC architectures using private IVM-based work stealing. The proposed approach has been extensively experimented on an Intel Xeon Phi 5110P using several instances of the Flow-Shop scheduling permutation problem. The reported results show that the IVM-based work stealing approach is about 10 times faster than the linked-list traditionally used for parallel B&B.