Multi-agent-based simulation for artificial stock market (ASM) is an important method in behavioural finance. The social network in ASM will influence the coordination and decision making of the intelligent agents. To improve the performance of an ASM with evolving social networks in a distributed computing environment, the computational load balancing and inter-nodes communication should be considered jointly. This paper proposes a scheduling algorithm called LBMIC to partition the agents onto different computing nodes while keeping the degree of load imbalance lower than a given threshold with minimized inter-nodes communication between agents. LBMIC models the scheduling into a graph partitioning problem and uses the multi-level graph partitioning algorithm to achieve an efficient scheduling. When the network evolves, LBMIC refines the partitioning by migrating parts of the agents. The experiments indicate that LBMIC can efficiently improve the performance of communication-intensive ASMs by both initial partitioning and refining partitioning.