Currently, software construction is moving towards industrialization by replacing the custom form of development with the use of approaches such as Software Product Lines (SPL) and Multiple Software Product Lines (MPL). These paradigms establish a common means of production to generate a variety of products through the reuse of inputs and automation of processes and thus meet the needs and requirements of the market instead of targeting specific customers. However, the management of multiple SPLs or MPLs is a challenge because the variant number of possible software products to be obtain expressed in the feature models is often large due to feature combinations. For this reason, it is not feasible to configure, deploy, or test all possible product variants. To support this decision-making process, this chapter investigates and applies a variant of the knapsack problem. Specifically, product configuration in an MPL is formulated as a Multiple-Choice Multi-dimensional Knapsack Problem (MMKP) and is solved with a Search-Based Software Engineering (SBSE) technique. First, a review of search and optimization techniques is provided to offer practical application elements for the development of software products based on SPL and Search-Based Software Engineering (SBSE). A genetic algorithm is then implemented in Python to solve the problem of configuring products in MPL.