In this paper we present two parallel skeltons for Tabu Search method --a well known meta-heuristic designed for finding approximate solution of combinatorial optimization problems. Our parallel skeletons are designed and implemented using the generic parallel programming paradigm. The first skeleton is based on independent runs while the second in the classical master-slave model. Our starting point is the design and implementation of a sequential skeleton that is used later as basis for the two parallel skeletons. Both skeletons provide the user with the followings: (a) permit to obtain parallel implementations of tabu search method for concrete combinatorial optimization problems from existing sequential implementations; (b) there is no need for the user to know neither parallel programming nor communication libraries; (c) the parallel implementation for a concrete problem is obtained automatically from a sequential implementation of the problem. The skeletons, however, require from the user a sequential instantiation of tabu search method for the problem at hand. The skeletons are implemented in C++ using MPI as a communication library and offer several properties such as a genericity, flexibility, component reuse, robustness and time savings, mainly due to the generic programming and object oriented programming paradigms. We have instantiated the two skeletons for the 0-1 Multidimensional Knapsack problem and have observed the claimed properties of the skeletons