Staff scheduling as per requirements and availability has been a major concern in healthcare systems for long time. This paper focuses on the specific researches targeting the problem of allocating nurses to patients in an effective manner. Most of the existing solutions are guided by various constraints concerning nurses and patients. We surveyed a lot of researched works in this paper in the domain of Nurse Scheduling Problem (NSP). It is a well researched classical scheduling problem in the domain of operation research. In literature, NSP were solved using various methods like integer programming, constraint programming, metaheuristic methods to mitigate the conflicts in scheduling of nurses. The objectives of the researchers were to maximize the fairness of the schedule, to reduce the cost, minimize the constraints, etc. We present a comparative analysis between various techniques by using Gurobi optimizer in Python 3.7 in terms of their objective function and time complexity. A case study has also been presented for the performance analysis. We have studied techniques based on bee colony optimization, simulated annealing, and memetic algorithm. The results are validated using a statistical analyzer. In future this may help to build more optimized schedules in this field of NSP. © 2019 IEEE.