The problem of fully distributed sequential change detection (FDSCD) is studied, where a bunch of distributed sensors collaboratively detect the occurrence of an abrupt event without the help of a fusion center. Due to the distributed nature, each sensor determines whether the event has occurred according to their own observations and messages received from their neighbors through noiseless links with finite capacity. In this paper, a novel algorithm is proposed for FDSCD that views every sensor as a fusion center and mimics the behavior of the cumulative sum (CUSUM) algorithm that is optimal in the presence of fusion center. One crucial factor in the proposed algorithm is the weighting factors that reduce the influence of stale messages circulated in the network. Design of these weighting factors based on genetic algorithms is provided. Quantizer design is also studied to meet the finite capacity constraint. Two designs are proposed where the first one numerically maximizes the total output Kullback-Leibler divergence and the second one again exploits the genetic algorithms. Extensive simulations are conducted whose results show that the proposed algorithm outperforms the current state-of-art in terms of the tradeoff between expected delay and average run length to a false alarm.