The Nurse Scheduling Problem (NSP) is a complex NP-hard combinatorial optimization task that involves assigning nursing staff to shifts while satisfying operational constraints such as staffing requirements, labor regulations, and personal preferences. Efficient and equitable scheduling is essential for effective healthcare decision-making. This paper proposes a Quantum-Inspired Genetic Algorithm (QIGA) that integrates quantum computing concepts—such as quantum state encoding and dynamic probability amplitude adjustment—into a classical genetic algorithm framework to enhance solution quality and search efficiency. An adaptive parameter control mechanism guides the algorithm from exploration to exploitation. Experimental results on 12 NSP instances of varying difficulty show that QIGA outperforms a standard GA, achieving better fitness values in nine instances and reducing computational time across all cases.