Multi-Installment Divisible-Load Scheduling model is a hot topic in the field of Big Data Processing in heterogeneous parallel and distributed systems. The effective division of data and the determination of scheduling strategy are the key and difficult problems. Minimizing the make-span of the entire divisible load is the primary objective of multi-installment scheduling in heterogeneous parallel and distributed systems. It has been demonstrated that the make-span is minimized when the processor sequence follows the order in which the link speed decrease in single-installment scheduling, however, the optimization of multi-installment divisible-load scheduling is a very hard problem. The descending order of link speeds is usually not an optimal order. To solve this problem, we propose a multi-installment scheduling model considering the processor order, and design an efficient global optimization genetic algorithm to solve the model. Experimental results show that the proposed algorithm has better performance than that of the compared multi-Installment methods.