Literature on nurse rostering and scheduling is extensive. Several studies have employed optimization methods to solve the NSP, like linear, integer or mixed integer programming, goal programming or constraint programming. Many of more recent paper tackle the NSP with met heuristic methods such as genetic algorithms, tab search or simulation. We believe the resolution techniques involving the use of solvers are more easily transferable to hospital-services. Other approaches, like heuristics or meta- heuristics are less accessible, and could be time-consuming. Hence our contribution, related to existing approaches, is focused on the linear programming problem, which seeks to satisfy the demand coverage while minimizing the salary cost and maximizing the nurses’ preferences as well as team balance. Different objectives are studied in this literature are to decrease manual scheduling, to increase demand covering in terms of workforce size but also according to required skills, to obtain equity between the schedules.