The cost of a fitness evaluation is often cited as one of the weaknesses of Search-Based Software Engineering: to obtain a single final solution, a meta-heuristic search algorithm has to evaluate the fitness of many interim solutions. Recently, a sampling-based approach called SWAY has been introduced as a new baseline that can compete with state-of-the-art search algorithms with significantly fewer fitness evaluations. However, SWAY has been introduced and evaluated only in numeric and Boolean decision spaces. This paper extends SWAY to permutation decision space: we start by presenting the theoretical formulation of the permutation decision space and the distance function required by SWAY and evaluate our formulation using the Test Case Prioritisation (TCP) problem as a benchmark. The results show that SWAY for permutations can be competitive against the additional greedy algorithm, which is the most widely used heuristic baseline for TCP.