Generation of optimum planar B-spline curve in terms of minimum deviation and required fairness to approximate a target shape defined by a strip-shaped unorganized 2D point cloud is studied. It is proposed to use the location of control points as variables within the geometric optimization framework of point distance minimization. An adaptive simulated annealing heuristic optimization algorithm is developed to iteratively update an initial approximate curve towards the target shape. The new implementation comprises an adaptive cooling procedure in which the temperature change is adaptively dependent on the objective function evolution. It is shown that the proposed method results in an improved convergence speed when compared to the standard simulated annealing method. A couple of examples are included to show the applicability of the proposed method in the surface model reconstruction directly from point cloud data.