Packing problems with performance constraints are categorized as combinatorial optimization problems with strong application background. This paper is concerned with the research on global optimization algorithms based solution for packing problems. Genetic algorithm as a kind of intelligent algorithm, can be used to solve problems of the global optimal solution but their efficiency is not quite satisfied due to their intrinsic NP-hard computational complexities. The paper improves the initial solu-tion of GA, and a multi-object optimization model is formulated on simplified satellite cabin packing problem. By comparison on a case of such packing problem constructed with traditional genetic algorithm which produces with random data, this algorithm is superior to the traditional GA and multiplier algorithm in the term of solution efficiency.% 带性能约束的三维布局问题属于具有很强应用背景的组合优化问题，进行了基于全局的布局求解方法的探索。由于NP完全问题的计算复杂性，使得遗传算法求解问题的全局最优解时效率较低。改进了遗传算法的初始解，对提高算法的效率进行了研究。并以旋转卫星舱布局的简化模型为背景，建立了多目标优化数学模型。实例结果与传统遗传算法以及乘子法的计算结果比较，表明该算法具有较好的求解效率。
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