Mathematical facility layout models have been employed to reduce unnecessary worker movement [20], while discrete event simulation enables the assessment of different configurations before implementation [21; 22]. Additionally, Evolutionary Algorithms have proven particularly suitable for optimizing resource allocation in constraint-laden processes [23; 24]. These approaches have been successfully applied in the automotive, consumer goods, and agribusiness industries, readily demonstrating that operation synchronization reduces costs and production times [25; 26; 27; 28]. However, their implementation in artisanal workshops remains limited, highlighting a gap in the application of these techniques within this sector. While large industries have integrated advanced planning and automation systems, artisanal workshops not only resist adopting new technologies [29] but also lack solutions tailored to their high variability. This makes it difficult to implement advanced production system optimization methodologies [30; 31]. Some studies have explored hybrid methodologies to optimize facility layout, such as the work of [PERSON] et al. [32], who integrated discrete event simulation with the Computerized Relative Allocation of Facilities Technique (CRAFT), achieving a 13.22% reduction in material flow costs and a 4.28% decrease in workflow time. However, these approaches represent an intermediate stage between traditional heuristics and advanced combinatorial models, underscoring the need for more adaptable methodologies for the artisanal sector. [PERSON] et al. [21] identified that initial solutions obtained through Systematic Layout Planning (SLP) can be improved with advanced tools such as Genetic Algorithms(GAs), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS). Additionally, simulation-based techniques such as Data Envelopment Analysis (DEA), and Multi-Criteria Decision Making (MCDM) complete the roster. Although these approaches have proven effective in industries with established infrastructure, their application in artisanal workshops remains a challenge due to spatial distribution variability and the absence of automated processes. In this context, Evolutionary Algorithms (EAs) have emerged as a more flexible alternative for layout optimization in highly dynamic environments such as the artisanal manufacturing system studied in this work. However, while the literature highlights the feasibility of applying computational optimization techniques in production systems with spatial and technological constraints, this remains an underexplored aspect for these microenterprises. This study poses the following research question: Can an Evolutionary Algorithm (EA) effectively optimize the workstation layout and worker movements in artisanal workshops? Despite advances in facility layout optimization using heuristic and metaheuristic approaches, important gaps remain in the literature. First, most studies focus on industrial settings with fixed workflows and automated operations, overlooking the complexity of artisanal systems with multiproduct environments and little to no automation. Secondly, although methodologies such as SLP, CRAFT, and hybrid models have shown efficiency in structured environments, their application in dynamic artisanal workstation configurations is limited. Thirdly, there is little empirical validation of Evolutionary Algorithms in real-world artisanal workshops, particularly in Latin American contexts characterized by spatial, technological, and organizational constraints. Finally, the literature lacks methodological frameworks adapted to simultaneous product manufacturing, where coordination between workers and stations must occur without advanced technologies. This study addresses these gaps by proposing and validating an Evolutionary Algorithm designed for flexible-tasking, low-automation, multiproduct artisanal workshops.