This paper aims at integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. Specifically, our study develops a novel efficient iterated greedy algorithm based on reinforcement learning. The main novelty of the proposed algorithm is its new perturbation mechanism, which incorporates Q-learning to select appropriate perturbation operators during the search process. Through an application to the permutation flowshop scheduling problem, comprehensive computational experiments are conducted on a wide range of benchmark instances to evaluate the performance of the proposed algorithm. This evaluation is done against non-learning versions of the iterated greedy algorithm and seven state-of-the-art algorithms from the literature. The experimental results and statistical analyses show the better performance of the proposed algorithm in terms of optimality gaps, convergence rate, and computational overhead.