Block-oriented sparse Cholesky factorization decomposes a sparse matrix into rectangular sub-blocks; each block can then be handled as a computational unit in order to increase data reuse in a hierarchical memory system. Also, the factorization method increases the degree of concurrency with the reduction of communication volumes so that it performs more efficiently on a distributed-memory multiprocessor system than the customary column-oriented factorization method. But until now, mapping of blocks to processors has been designed for load balance with restricted communication patterns. In this paper, we represent tasks using a block dependency DAG that shows the execution behavior of block sparse Cholesky factorization in a distributed-memory system. Since the characteristics of tasks for the block Cholesky factorization are different from those of the conventional parallel task model, we propose a new task scheduling algorithm using a block dependency DAG. The proposed algorithm consists of two stages: early-start clustering, and affined cluster mapping. The early-start clustering stage is used to cluster tasks with preserving the earliest start time of a task without limiting parallelism. After task clustering, the affined cluster mapping stage allocates clusters to processors considering both communication cost and load balance. Experimental results on the Fujitsu parallel system show that the proposed task scheduling approach outperforms other processor mapping methods.