Scheduling scientific workflows in cloud computing environments, taking infrastructure provisioning into account, represents a complex optimisation problem, where minimising the total workflow completion time is a primary objective. This problem is computationally challenging, and metaheuristic methods such as genetic algorithms have been widely employed to obtain approximate solutions. In this study, an alternative approach based on a local search algorithm is proposed, employing four neighbourhood structures. These structures operate on the ordering of task assignments to virtual machines, enabling efficient exploration of the solution space through small, targeted modifications. A hybrid metaheuristic, known as a memetic algorithm, is used, combining the global search capabilities of genetic algorithms with the local refinement provided by the proposed local search. Experimental results demonstrate that the memetic algorithm produces competitive results compared with traditional genetic algorithms.