Spatial databases are entering an era of mass deployment in various real-life applications, especially mobile and location-based services. The real-time processing of spatial queries to meet different performance goals poses new problems to the real-time and parallel processing communities. In this paper, we investigate how multiple window queries can be parallelized, decomposed, scheduled and processed in realtime workloads to optimize system performance, such as I/O cost, response time and miss rate. We devise in-memory R-trees to decompose queries into independent jobs. Jobs from different queries can be combined according to their spatial locality to eliminate redundant I/Os. Runtime job schedulers are elaborately devised to optimize response time or miss rate for various systems. Empirical results show a significant performance improvement over the sequential, unparalleled approach.