This study presents an evolutionary computation system that can generate grid robot path planning problems. An evolvable cellular representation that specifies how to build a PPP is used. Also presented is a technique for taxonomizing path planning problems so that the vast number of problems that can be generated with the evolutionary computation system can be subsequently winnowed into a collection of substantially different problems of specified size. In this study the most difficult path planning problems, according to three different criteria, are evolved and those results are used to demonstrated the taxonomic technique. The hardness criteria are (i) the minimum number of turns a robot must make, (ii) the minimum number of forward moves it must make, and (iii) the sum of these quantities. A dynamic programming algorithm is used to compute these quantities for a given path planning program. The technique can be generalized to find cases of a specified hardness. The size of the board and maximum number of obstacles used are transparently specifiable.