SPARQL property path queries provide a succinct way to write complex navigational queries over RDF knowledge graphs. However, their evaluation remains difficult as they may involve the execution of transitive closures. As a result, many property path queries just timeout when executed on public online RDF knowledge graphs. One solution to speed up their execution is to find optimal join orders. Although the join ordering problem has been extensively studied for traditional SPARQL queries, the presence of property path patterns biases existing approaches. In this paper we focus on C2RP QUF queries (conjunctive SPARQL property path queries with UNION and FILTER), and we present a query optimizer that is able to capture the cost of C2RP QUF queries using an appropriate cost model and a sampling-based cardinality estimator. On the latest Wikidata Query Benchmark, we empirically demonstrate that our approach finds significantly better join orders than Virtuoso and BlazeGraph.