Recently, a new fruit fly optimization algorithm (FOA) is proposed to solve stream-shop scheduling. In this paper, we empirically study the performance of FOA. The experimental results illustrate that FOA cannot solve set-streaming stream-shop scheduling in distributed system with same-size sub-sets effectively. In order to enhance the performance of FOA, an amended FOA (named aFOA) is proposed. Numerical testing proves and comparisons of aFOA with FOA and GA show that aFOA can greatly enhance the scheduling efficiency and greatly improve the scheduling quality. The resolving the set-streaming stream-shop scheduling in distributed system (SSSS) with same-size sub-sets by mean of an amended fruit fly optimization algorithm (aFOA) is intended in this paper. In the intended aFOA, a result is delineated as two vectors to find the dividing of tasks and the sequence of the sub-sets simultaneously. An aFOA is based on the encoding system three kinds of neighborhoods are developed for generating new results. To considerably balance the development and exploration, including the neighborhood-based search (smell-vision-based search) and the global cooperation-based search, two main search processes are designed within the evolutionary search model of the aFOA. Finally, on the basis of numerical testing results are provided, and the comparisons demonstrate the effectiveness.