多芯片模块（MCM）封装是提升集成电路性能与集成度的关键技术，其布局设计对系统性能与成本具有重要影响。传统依赖仿真和实验的设计方法效率低下，难以实现在复杂设计空间中的有效权衡。本文提出一种融合自动化仿真、代理模型与多目标优化算法的散热-成本协同设计策略。首先，利用Python二次开发实现数值模拟的全流程自动化，大幅提升仿真效率；然后，建立芯片间距与最高温度间的高精度代理模型，基于支持向量回归（SVR）实现热性能的快速预测；在此基础上结合MCM成本模型，采用改进的NSGA-II多目标算法高效搜索Pareto最优前沿。研究结果表明，所获得的Pareto前沿曲线优于原始布局方案，自动化框架和智能算法显著提升了MCM协同设计效率，为散热性能与成本的权衡提供依据。

As a key technology for improving the performance and integration density of integrated circuits, the layout design of Multi-Chip Module (MCM) packaging has a significant impact on system performance and cost. Traditional design methods relying on simulations and experiments are inefficient and difficult to achieve effective trade-offs in complex design spaces. This paper proposes a thermal performance and cost collaborative design strategy integrating automated simulation, surrogate models, and multi-objective optimization algorithms. First, Python secondary development was used to realize the full-process automation of numerical simulations, greatly improving simulation efficiency; then, a high-precision surrogate model between chip spacing and maximum temperature was established, and fast prediction of thermal performance was achieved based on Support Vector Regression (SVR); on this basis, combined with the MCM cost model, the improved NSGA-II multi-objective algorithm was adopted to efficiently search for the Pareto optimal front. The research results show that the obtained Pareto front curve is superior to the original layout scheme, and the automated framework and intelligent algorithms significantly improve the efficiency of MCM collaborative design, providing a basis for the trade-off between thermal performance and cost.