From the designer perspective, in real-time applications as mechatronic systems - the control algorithm has to be easy to understand and to implement, easy to optimize and last but not least, to ensure the best possible behavior for the controlled system. The paper presents a design environment which provides the possibility to choose between a classical control algorithm (as state feedback stabilization represents) and a new approach (interpolative controllers) with the possibility to modify the designed parameters such as to obtain the optimum behavior for the controlled system. This opportunity is given through the inner structure and operating laws of the algorithm and exploited using genetic algorithm-based techniques. Interpolative-type controller category covers the controllers based on fuzzy, neural and pure interpolative algorithms, due to their common capability to make an approximate reasoning. The first ones, fuzzy and neural algorithms, are already well-known in the control area. A pure interpolative controller presumes to contain at least one block, placed no matter where in his structure, in which interpolation as mathematical operation to have place. The approach presented in the present paper uses this kind of blocks to reproduce and to optimize the behavior of an already existing controller (in this case the state feedback one) [1]. This type of controllers meets all the requirements stated at the beginning: they are easy to implement, easy to understand, they need reduced calculus time and gives notable results in specific cases. The above mentioned kind of controllers will be presented with all the necessary details in the paper. The design environment is presented as a collection of MATLAB functions, SIMULINK configurable schemes and a user interface. A case study ends the presentation with some experimental results obtained through a simulation for a specific second order positioning system.