Efficient task scheduling is fundamental for the success of the Grids, since it directly affects the Quality of Service (QoS) offered to the users. Efficient scheduling policies should be evaluated based not only on performance metrics that are of interest to the infrastructure side, such as the Grid resources utilization efficiency, but also on user satisfaction metrics, such as the percentage of tasks served by the Grid without violating their QoS requirements. In this paper, we propose a scheduling algorithm for tasks with strict timing requirements, given in the form of a desired start and finish time. Our algorithm aims at minimizing the violations of the time constraints, while at the same time minimizing the number of processors used. The proposed scheduling method exploits concepts derived from spectral clustering, and groups together for assignment to a computing resource the tasks so to a) minimize the time overlapping of the tasks assigned to a given processor and b) maximize the degree of time overlapping among tasks assigned to different processors. Experimental results show that our proposed strategy outperforms greedy scheduling algorithms for different values of the task load submitted.