The process of resource management in local and wide-area distributed systems involves three main phases: resource discovery, resource scheduling and resource allocation. This paper presents a heuristic resource scheduler to cover the scheduling phase in large-scale distributed systems such as Grids. This approach is supported by BLOMERS (Balanced Load Multi-Constrain Resource Scheduler). Our heuristic scheduler implements a Genetic Algorithm (GA) in order to improve the scalability of the system, in largescale Grids the amount of resource is massive but their computational capacity is low. Therefore, selection of computational resources to allocate customers services becomes an NP-hard problem. The paper outlines a performance evaluation by means of experimental tests. The results highlight two aspects: The scheduling system is reliable in real large-scale Grids and BLOMERS heuristic scheduler offers better performance than common selection algorithms when the number of resource requests is increasing.