Recently, the sparse vector code (SVC) is emerging as a promising solution for short-packet transmission in massive machine type communication (mMTC) as well as ultra-reliable and low-latency communication (URLLC). In the SVC process, the encoding and decoding stages are jointly modeled as a standard compressed sensing (CS) problem. Hence, this paper aims at improving the decoding performance of SVC by optimizing the spreading matrix (i.e. measurement matrix in CS). To this end, two greedy algorithms to minimize the mutual coherence value of the spreading matrix in SVC are proposed. Specially, for practical applications, the spreading matrices are further required to be bipolar whose entries are constrained as +1 or -1. As a result, the optimized spreading matrices are highly efficient for storage, computation, and hardware realization. Simulation results reveal that, compared with the existing work, the block error rate (BLER) performance of SVC can be improved significantly with the optimized spreading matrices.