Diversity in teams has become an important societal and economic issue which is studied in various scientific domains. In organizational sciences, particularly empirical research methods prevail. This paper proposes to explore agent-based computational economics as a research approach for workforce diversity more intensely due to its inherent properties like capturing heterogeneous interacting agents. For highlighting this, this paper presents an agent-based computational model based on the framework of NK fitness landscapes. In the simulations, artificial organizations search for superior levels of organizational performance with search being delegated to several and potentially diverse decision-making agents. The experiments control for the level of task complexity and reflects four different attributes of workplace diversity among agents: cognitive capabilities to (i) generate and (ii) evaluate new solutions, (iii) effort efficiency and (iv) commitment to the overall organizational objective. The results suggest that the effects of workforce diversity differ across task complexity and attributes of diversity. Diversity of commitment has the strongest impact which results from interactions among local maximizers and agents seeking to globally maximize with only local means. Moreover, the results point to nonlinear effects of multi-attributive diversity on organizational performance.