When big data flow calculation tasks with different attributes generated by networked vehicle nodes are transmitted and offloaded， issues such as time delay jitter， large computational energy consumption and system overhead usually happen. Therefore， according to the actual communication environment， a scheme for task offloading and resource allocation based on Simulated Annealing Algorithm （SAA） in Cellular Vehicle to Everything （C-V2X） Internet of Vehicles （IoV） was proposed. Firstly， according to the task processing priority， the tasks with high processing priority were processed by collaborative offloading and computing. Secondly， an SAA-based task offloading strategy was developed with the aid of globally searching for the optimal offloading scale factor. And the task offloading scale factor was analyzed and optimized. Finally， during the update process of task offloading scale factor， the problem of minimizing the system overhead was transformed into the convex optimization problem of power and computational resource allocation. And the Lagrange multiplier method was used to obtain the optimal solution. By comparing the proposed algorithm with the local offloading and adaptive genetic algorithm， it can be seen that： as the calculation task data size increases， the time delay， power consumption and system overhead of the adaptive genetic algorithm are decreased by 5.97%， 49.40%， and 49.36% respectively， compared with those of the local offloading. On this basis， the time delay， power consumption and system overhead of the proposed SAA-based scheme are further decreased by 6.35%， 92.27%， and 91.7% respectively， compared with those of the adaptive genetic algorithm. As the CPU cycles of the calculation task increase， the time delay， power consumption and system overhead of the adaptive genetic algorithm are decreased by 16.4%， 49.58%， and 49.23% respectively， compared with local offloading. On this basis， the time delay， power consumption and system overhead of the proposed SAA-based scheme are further decreased by 19.61%， 94.39%， and 89.88% respectively， compared with those of the adaptive genetic algorithm. Experimental results show that SAA cannot only reduce the time delay， power consumption and system overhead of communication systems but also accelerate convergence of the results.