In parallel tree search environments, it is likely that some nodes are heavily loaded while others are lightly loaded or even idle. It is desirable that the workload is fully distributed among all nodes to utilize the processing time and optimize the whole performance. A load balancing mechanism decides where to migrate a process and when. This paper introduces a load balancing mechanism as a novel scheme to support the reliability and to increase the overall throughput in parallel tree search environments. To alleviate the effects of processor idleness and non-essential work, a dynamic load balancing scheme called Preemptive Polling Scheme (PPS) is proposed. This scheme employs a near-neighbor quantitative load balancing method by detecting and correcting load imbalance before they occur during the search. Message Passing Interface (MPI) parallel communication model was used for inter processor communication. The search algorithm was employed to solve a Discrete Optimization Problem (DOP) (Knapsack problem) on the multi-processor system. The results show that PPS brings about scalability and efficiency in the system, as there is time reduction in the execution of the parallel tree search algorithm over its sequential counterpart is solving the DOP.