Device-to-Device (D2D) collaborative offloading is a critical task offloading paradigm in mobile edge computing (MEC) environments, addressing the challenges of wasted idle device (ID) resources and the limited computational power of the edge server (ES). However, the constant mobility of devices leads to dynamic changes in the channel state and network topology. The process of offloading dependent tasks involves significant data transmission, and changes in the network environment can result in transmission failures that affect the entire task offloading process. This situation poses challenges to the effectiveness and stability of dependent task offloading. Therefore, we investigate the problem of dependent task offloading on multicore computing nodes in dynamic D2D environments. We formalize the problem as a mixed-integer nonlinear programming problem, which takes device mobility, task dependency, and user selfishness into account. To solve this problem, we propose a task offloading algorithm based on genetic algorithm (GA) and design a prioritization algorithm that matches the offloading decision to determine the execution order of subtasks. The results of simulation experiments on the Alibaba clustering dataset and synthetic dataset show that our algorithm significantly reduces the cost of task offloading compared to existing algorithms.