Offshore oil and gas exploration companies frequently need to deal with problems related to the efficient use of their resources. In this work, we address a ship scheduling problem associated with offshore oil and gas logistics – The Pipe Laying Support Vessel Scheduling Problem (PLSVSP). These vessels are specially designed to perform pipeline connections between sub-sea oil wells and production platforms. The connections are the last step to be performed to allow the oil draining, starting production in a well. The PLSVSP objective is to anticipate the completion of the most productive wells. The problem can be seen as a variant of a batch scheduling problem with identical parallel machines and non-anticipatory family setup times to minimize the total weighted completion time. In this analogy, vessels are machines, wells are jobs, and batches are voyages executed by PLSVs, defining which wells to connect each time it leaves the port. We developed several optimization approaches to solve the deterministic and stochastic variants of the problem. For the deterministic problem, we developed hybrid methods and a metaheuristic that outperformed the pure MIP formulations, being practical to deal with the PLSVSP. A simheuristic using embedded Monte Carlo simulation was developed for the stochastic variant of the problem, considering uncertainties in the connection duration and the arrival dates of pipelines at the port. The results show a significant improvement in the solutions dealing with uncertainties compared to solutions generated by a deterministic method. The use of simulation within a metaheuristic framework proved to be a promising approach, being able to deal with the stochastic problem, with little extra computational effort required.