<![CDATA[As the industry grows massively, the profits sought are also maximized by minimizing the cost of expenditure. One of the company's activities in minimizing the cost of expenditure is by optimizing the goods distribution process. Looking for patterns of arrangement of goods in the optimal goods distribution process is necessary to avoid the cost of such expenses incurred additional vehicle accommodation costs, overload of goods or even require more time if not using the system. Therefore, this research is done to overcome the problem of the preparation of goods in the car box for optimal use of genetic algorithm. Genetic algorithm is chosen because it can solve complex problems with a relatively fast time. In this study begins with initial individualization with random raised. The representation used is an integer representation. Then the process of reproduction of crossover and mutation. The crossover and mutation methods used by Partially Mapped Crossover and Reciprocal Exchange Mutation. Next is the selection process using Elitsm Selection. Based on the results of the tests that have been done, obtained accuracy of 84,6% with the best parameters that is on the population amount of 100, cr 0.6, mr 0.4 and generation 90. The final result obtained is optimization recommendations optimize the preparation of goods.]]>
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