In design and optimization of a complex system, there exist various methods for defining the relationship between the system as a whole, the subsystems and the individual components. Traditional methods provide requirements at the system level which lead to a set of design targets for each subsystem. Meeting these targets is sometimes a simple task or can be very difficult and expensive, but this is not captured in the design process and therefore unknown at the system level. This work compares Requirements Allocation (RA) with Distributed Value Driven Design (DVDD). A computational experiment is proposed as a means of evaluating RA and DVDD. A common preliminary design is determined by optimizing the utility of the system, and then a Subsystem of Interest (SOI) is chosen as the focal point of subsystem design. First the behavior of a designer using Requirements Allocation is modeled with an optimization problem where the distance to the design targets is minimized. Next, two formulations of DVDD objective functions are used to approximate the system-level value function. The first is a linear approximation and the second is a nonlinear approximation with higher fidelity around the preliminary design point. This computational experiment is applied to a series hybrid vehicle where the SOI is the electric motor. In this case study, RA proves to be more effective than DVDD on average. It is still possible that the use of objectives is superior to design targets. This work shows that, for this case study, a linear approximation as well as a slightly higher fidelity approximation are not well suited to find the design alternative with the highest expected utility.