This paper addresses the Permutation Flow Shop Scheduling Problem with Heterogeneous Workers (PFSP-HW), an extension of the classical problem in which processing times depend not only on the job and machine, but also on the assigned worker. This variant better reflects practical environments where worker capabilities and proficiencies vary significantly. We propose a new Iterated Greedy (IG) heuristic adapted to handle worker heterogeneity. The IG heuristic combines destruction and reconstruction mechanisms with a local search procedure tailored for the problem. We develop two versions of the proposed algorithm and compare them with adapted state-of-the-art heuristics and metaheuristics from related problems. The algorithms were tested on a large benchmark set comprising 360 instances generated under various shop configurations. The suggested IG heuristics surpass current approaches in terms of solution quality and execution time, as determined by computational and statistical evaluations, making them reliable and efficient tools for solving the PFSP-HW.